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Chemical Physics
Experimental, computational, and theoretical physics of atoms, molecules, and clusters - Classical and quantum description of states, processes, and dynamics; spectroscopy, electronic structure, conformations, reactions, interactions, and phases. Chemical thermodynamics. Disperse systems. High pressure chemistry. Solid state chemistry. Surface and interface chemistry.
Comb-referenced double-resonance and Lamb-dip methods measure level differences fitted to absolute term values
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Methane is a key spherical-top molecule, yet restrictive selection rules for one-photon transitions have prevented determination of its ground state (GS) energies with state-of-the-art kHz-level accuracy. We report the GS rotational energy level differences with kHz-level accuracy from two frequency-comb-referenced sub-Doppler methods: optical-optical double-resonance spectroscopy in the ${\Lambda}$-type configuration, and Lamb-dip spectroscopy of allowed and forbidden transitions. A Hamiltonian fit to the data yields GS term values with rotational numbers up to $\it{J}$ = 12 with kHz level accuracy.
A transient boron-carbon contact stabilizes an intermediate and places the transition near a nonradiative funnel for solar energy storage.
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The planar to Dewar valence isomerisation of 4a,8a-azaboranaphthalene (BN$_\text{Naph}$), a $\pi$ extended BN-doped analogue of azaborine, is investigated to evaluate how BN incorporation reshapes the minimum energy pathway on the ground state. This process is, for example, relevant in the context of molecular solar thermal (MOST) energy storage, where absorbed sunlight is converted into chemical energy through reversible photoisomerisation. Structures and vertical excitations were computed using DFT and TD-DFT, minimum energy pathways were mapped with nudged elastic band (NEB) calculations, and pathway energetics were refined with state averaged XMS-CASPT2. In addition, azaborine was examined as a comparison system, with particular emphasis on whether substituents at nitrogen and boron promote Dewar formation. The effect of BN doping on the system was analysed in detail. Compared with the carbon analogue, the conversion pathway becomes asymmetric with a metastable intermediate stabilized by a transient boron to carbon contact. The transition structure closely resembles an S$_0$/S$_1$ conical intersection, which is consistent with a vibrationally activated nonradiative funnel. For tuning MOST properties, screening of single substituents across the whole molecule reveals predominantly red shifted S$_1$ energies together with increased oscillator strengths and indicates that appropriate substitution can improve Dewar formation in azaborine derivatives.
We introduce two-quantum (2Q) fluorescence-detected pump-probe (F-PP) spectroscopy as a tool to probe ultrafast multiparticle interactions in many-body systems. We describe a pulse-shaper-based fully collinear setup utilizing phase cycling to capture the 2Q F-PP signal simultaneously with the one-quantum (1Q) F-PP signal. Thus, we investigate the dynamics of energy transfer and diffusion-limited annihilation. We apply a data post-processing strategy to isolate excited-state dynamics from spurious background. The technique is applied to a squaraine heterodimer and a squaraine copolymer to demonstrate the removal of so-called incoherent mixing that generally plagues action-detected nonlinear spectroscopy on multichromophoric systems. Specifically, we show that this approach is not only applicable to 1Q but also to 2Q F-PP signals, eliminating incoherent mixing contributions as well as other "parasitic" signals that result from pulse-overlap ambiguities. As a result, we retrieve background-free spectral and dynamical information of doubly excited electronic states.
Ammonia has strong poisoning effects on cathode catalyst layers of proton exchange membrane (PEM) fuel cells, but the poisoning mechanism is still unclear. In this study, all-atom molecular dynamics simulations are employed to investigate the poisoning mechanisms of ammonia. The results show that ammonium can replace the hydronium ions at the charged sites of sulfonic acid group of the ionomer side chain, and the adsorption of ammonium to sulfonic acid group can be attributed to van der Waals force and electrostatic interaction. Furthermore, other ammonia derivatives, amino and imino ions, can capture hydronium ions to form ion clusters. These ion clusters have strong capability to absorb hydronium ions, and their structures change with ammonia content and temperature. The main mechanism of formation of these clusters is due to the formation of relatively stable hydrogen bonds between ions within the clusters. These mechanisms significantly reduce the efficiency of proton transport, thereby decreasing the catalyst layer's performance in electrochemical reactions. We also discover that the increase in temperature leads to the dissociation of large ion clusters, the blockage in the ionomer layer can be alleviated, and the proton transport efficiency can be restored. The understanding of the poisoning mechanisms obtained in this study is helpful for subsequent research aimed at resolving ammonia poisoning and enhancing the anti-poisoning performance of catalyst layers.
Geometrical imperfections in quadrupole mass filters introduce higher-order field components that can significantly influence device performance, particularly under non-sinusoidal excitation. In this work, a comprehensive simulation study is carried out to investigate the effect of geometrical imperfections on the performance of a rectangular wave driven quadrupole mass filter operating in the first stability zone. Radial field distortions arising from controlled variations in rod geometry and position, including single rod radius variation, single rod displacement, diagonal rod radius variation, and diagonal rod displacement, are examined. These imperfections introduce octupole field components that distort the ideal quadrupolar field distribution. The influence of such distortions on key performance parameters, namely mass resolution and ion transmission efficiency, is systematically evaluated. The results show that the presence of radial asymmetry leads to a degradation of both resolution and transmission efficiency in all cases considered. Furthermore, the study reveals a strong dependence of mass filter performance on the initial state of the applied pulsed waveform, specifically whether the asymmetric rod pair is subjected to the high or low level of the RF pulse. These findings provide important insights into the tolerance limits of geometrical imperfections and their impact on the performance of pulsed wave driven quadrupole mass filters, which are relevant for the design and optimization of high-resolution digital mass filtering systems.
Simulations link aluminum-rich gel surfaces to stronger retention of Pb2+, Ba2+, and Cs+ via specific oxygen coordination.
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Cementitious materials are widely used for hazardous-waste encapsulation, yet the molecular mechanisms governing heavy-metal ion retention across different gel chemistries remain insufficiently resolved. Here, classical molecular dynamics simulations were employed to investigate the adsorption-controlled mobility of representative heavy-metal ions (Pb2+, Ba2+, and Cs+) within nanopores of C-S-H, C-(N)-A-S-H, and N-A-S-H gels. By combining pore-averaged diffusivity, spatially resolved diffusivity and residence-time analysis, ion-density profiles, two-dimensional adsorption maps, radial distribution functions, coordination analysis, and interfacial binding-strength descriptors, this study establishes a comparative atomistic framework linking gel surface chemistry to ion mobility suppression under nanoconfinement. Ion mobility is substantially reduced in all gel nanopores relative to bulk solutions, but the extent and mechanism of suppression vary strongly with gel chemistry. C-(N)-A-S-H with higher Al/Si ratios exhibits the strongest retention, driven by ion accumulation around Al-linked oxygen species via an ion-exchange-like mechanism with charge-balancing Na+. C-S-H immobilizes ions primarily through surface hydroxyl oxygens and Ca-mediated linkages, whereas N-A-S-H exhibits more distributed binding environments. Pb2+ and Ba2+ exhibit broadly similar immobilization mechanisms, whereas Cs+ shows more distinct, gel-dependent interactions with silicate and aluminosilicate oxygen sites. A relative total binding strength (rTBS) descriptor is introduced, showing a strong positive correlation with the extent of ion immobilization across gel types, ion species, and pore sizes examined. These results clarify gel-specific and ion-specific mechanisms controlling heavy-metal retention in idealized cementitious nanopores.
We present an efficient implementation of a one-step relativistic second-order multireference perturbation theory based on the multireference driven similarity renormalization group (MR-DSRG) using the exact two-component (X2C) Hamiltonian, which we denote X2C-DSRG-MRPT2. We show that the X2C-DSRG-MRPT2 method can accurately capture spin--orbit coupling (SOC) effects in the electronic structure of strongly correlated systems containing elements across the periodic table. We further demonstrate that the X2C-DSRG-MRPT2 method, through its variational treatment of SOC effects, can yield spin--orbit splittings with mean absolute percentage errors consistently below 7% with respect to experimental values for systems containing up to sixth row elements. With its modest computational scaling (fifth power in system size) and high accuracy, X2C-DSRG-MRPT2 provides a promising avenue for the routine treatment of relativistic effects in strongly correlated molecular systems.
In this work, we develop a size extensive Auxiliary-Field Quantum Monte Carlo (AFQMC) approach that scales as $O(N^5)$ for local energy evaluation by treating the Coupled Cluster Singles and Doubles (CCSD) trial wavefunctions perturbatively. Comprehensive numerical examinations, spanning from main-group molecules to $3d$ transition metal complexes, demonstrate that this perturbative treatment introduces negligible bias. For small systems, our method achieves an accuracy and level of noise comparable to AFQMC with configuration interaction singles and doubles (CISD) trial wavefunctions while outperforming CCSD(T). This size extensivity offers a decisive advantage for large systems, as suggested by the ground state energies of non-interacting monomers and one-dimensional atomic chains. Finally, the numerical simulations of the uniform electron gas (UEG) provide evidence that, unlike the CCSD(T) method, our new approach does not suffer from infrared divergence in the thermodynamic limit (TDL).
Two-body reduced density matrices (2RDMs) encode the essential two-electron physics of electronic states, but their quartic storage cost poses a major limitation in practical workflows. We investigate a simple protocol to compress both transition and non-transition 2RDMs into a lower-rank representation that preserves their wedge-product structure and physical symmetries under truncation. The resulting decomposition couples Coulomb and exchange channels through a common set of low-rank factors, yielding a more compact rank-sparse representation than single-channel factorizations. For correlated states, the effective rank scales linearly with system size, achieving a $\sim99$\% compression for the coupled-cluster 2RDM of octane while retaining chemical accuracy. We apply this to the recently introduced {\em ab initio} eigenvector continuation workflows, where many-body wave functions are interpolated across nuclear geometries with mean-field cost. Here, 2RDMs between training states act as projectors into a subspace but their memory scaling limits applications to larger systems. The compression scheme reduces the memory cost from quartic to quadratic for a fixed error per electron. Metrics to systematically control the decomposition are investigated, enabling statistically resolved structural, dynamical and spectroscopic observables from nonadiabatic molecular dynamics simulations of photoexcited H$_{28}$ chains, interpolating from compressed near-exact DMRG training data. This establishes these structure-preserving compressed intermediates for practical correlated electronic structure workflows.
Null points in synthetically tunable molecular aggregates are predicted to generate flat energy bands analogous to those known in strongly correlated condensed-matter physics. For chemistry, null points provide a powerful design principle for photovoltaic materials with selective charge filtering similar to photosynthesis. However, null points have never been experimentally verified because their defining prediction - state localization with selective electron or hole transfer - has remained unobserved. Here, using a donor-acceptor dyad as a minimal model, we provide the first experimental observation of a null point. Impulsive pump-probe measurements reveal charge separation through a near-instantaneously generated locally excited-charge transfer (LE-CT) intermediate that emerges upon solvent stabilization of CT states. Polarization anisotropy directly reveals state localization and selective charge-filtering, spanning balanced electron-hole transfer to selective hole filtering consistent with synthetic design. A generalized vibronic theory of null points explains these observations and identifies the ideal synthetic parameters for achieving null points which are protected from the vibrational bath.
Nuclear magnetic resonance (NMR) spectroscopy provides an experimental readout of local chemical environments, but its use in molecular representation learning has been constrained by heterogeneous data and incomplete atom-level assignments. Here we construct complementary high-fidelity experimental and computational 13C NMR resources, which reveal a recurrent form of representational collapse: atoms that are equivalent in molecular topology can remain experimentally distinct in their real chemical environments, whereas explicit 3D descriptions are further limited by static conformations in dynamic regimes. To alleviate this bottleneck, we develop CLAIM (Contrastive Learning for Atom-to-molecule Inference of Molecular NMR), a framework that aligns efficient topological molecular inputs with atom-resolved NMR observables. Through hierarchical chemical priors and cross-level contrastive learning, CLAIM restores lost chemical resolution and markedly improves atom-level molecule-spectrum retrieval. CLAIM remains robust in flexible and tautomeric systems for 13C NMR prediction, improves stereoisomer discrimination without explicit 3D modelling, and transfers to broader molecular property tasks including ADMET prediction and fluorescence estimation. These results establish physically grounded spectral alignment as an effective strategy for alleviating chemical-environment collapse and for guiding experimentally grounded molecular representation learning.
The accurate description of electron correlation is a central challenge in computational chemistry, with selected configuration interaction (SCI) emerging as a powerful tool to approach the full CI limit. While recent machine learning (ML) integrations have accelerated determinant selection, existing regression and classification approaches suffer from a fundamental objective-loss mismatch: they evaluate the importance of determinants in isolation without explicitly accounting for their relative importance ranking. Here, we introduce ranking configuration interaction (RCI), a novel ML-supported SCI framework that reframes determinant selection as a pairwise ranking problem. Building upon a Transformer-based architecture to capture complex, non-local orbital dependencies, RCI progressively optimizes the partial ordering of determinants. By doing so, RCI aligns the training objective more closely with the intrinsic ranking nature of SCI. Extensive benchmarks across both plane-wave and Gaussian basis sets, including the molecules N$_2$, CO, H$_2$O, NH$_3$, and C$_2$, demonstrate the substantial efficiency of RCI. Compared to previously reported classification baselines, RCI consistently accelerates convergence-reducing overall computational time by 23% to over 50% depending on the system, and requiring only 55% of the determinant count in representative cases such as N$_2$ and CO. Furthermore, RCI exhibits robust performance and reaches chemical accuracy on the highly challenging iron-sulfur using only 12% of the full CI space. Notably, RCI outperforms recent regression-based SCI methods by delivering either further 15% improvement in accuracy at comparable determinant counts, or 15% gain in compactness at similar accuracy. This pairwise learning-to-rank model provides a lightweight and modular plugin that can be seamlessly incorporated into other supervised-learning frameworks.
We study a simple but useful test for neural exchange-correlation (XC) functionals: can a neural model reproduce an established XC functional when it is used self-consistently? We call this test functional cloning. The model is trained at the GGA level to reproduce a known semilocal functional, using either a constrained or an unconstrained architecture. The motivation is that an XC functional is not used on a fixed input. In a Kohn-Sham self-consistent-field calculation it contributes to the potential, and the resulting density is part of the outcome of the same calculation. A good pointwise fit to sampled density descriptors is therefore not by itself enough. Because the target functional is known, the error can be measured directly. We compare the clones on sampled descriptors, molecular total energies, energy differences, transfer between PySCF and SIESTA, and equations of state for crystalline solids. The constrained models reproduce the reference functional more accurately in molecular self-consistent calculations. They also give better initial parameters for later optimization against correlated molecular energies. An additional observation is that the constrained architecture already gives a reasonable solid-state baseline before cloning, as seen from randomly initialized constrained models. Clones trained only on molecular densities transfer well to solids, reproducing reference lattice constants and bulk moduli across metallic, covalent, ionic, oxide, and layered systems. Cross-code tests show that energy differences are relatively robust, while total energies depend strongly on whether the cloning descriptors come from all-electron or pseudopotential densities. These results make functional cloning a useful diagnostic before full self-consistent training of neural XC functionals.
Calculations on thymine-water clusters show the 1π* state lasts 110 fs instead of 39 fs once three waters are added.
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We investigate microhydration effects on the three low-lying {\pi}* shape resonances of thymine using the Resonance via Pad\'e approach in combination with the DLPNO-EA-EOM-CCSD method. For isolated thymine, the calculated resonance positions are benchmarked against projected CAP-EA-EOM-CCSD calculations and compared with available theoretical and experimental data. Upon hydration, the 1{\pi}* and 2{\pi}* resonances undergo systematic stabilization accompanied by significant increases in their lifetimes, whereas the 3{\pi}* resonance exhibits a more complex behavior. In particular, the lifetime of the lowest resonance increases from 39 fs in isolated thymine to 110 fs in the thymine(H2O)3 cluster. Detailed analysis reveals that the observed resonance shifts arise from competing contributions involving hydrogen bonding, electrostatic interactions, microsolvation-induced geometric distortion, and finite-basis-set effects. Ghost-atom calculations demonstrate that diffuse basis functions associated with nearby water molecules contribute appreciably to the apparent stabilization, while explicit inclusion of water molecules leads to genuine physical stabilization of the resonance states. Furthermore, calculations on multiple conformers of the monohydrated cluster show that resonance positions and lifetimes depend strongly on the local hydrogen-bonding arrangement and microsolvation geometry. These findings demonstrate that resonance stabilization in microhydrated nucleobases is governed by a subtle interplay between geometry, basis-set effects, and intermolecular interactions.
Specialist functional trained on eight configurations transfers to rival PBE on WATER27 using only one binding energy
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In density functional theory, simpler exchange-correlation (XC) approximations such as the local density approximation (LDA) are favored for computational speed but rely on limited information, leading to a trade-off between accuracy and generality. Machine-learned XC approximations have seen a lot of interest to address this problem. Here, we train a neural network LDA using a differentiable Kohn-Sham solver, imparting system-specific expertise for water and sacrificing generality for accuracy. Our model achieves 1 kcal / mol errors on gold standard coupled cluster ionization and atomization energies, and improves predictions of spectral lines, electron density distribution, and equilibrium geometry from as few as eight configurations used for training. We proceed to perform transfer learning and obtain results comparable to higher-rung PBE and B3LYP functionals on the WATER27 subset of the GMTKN55 database, even when only a single two-molecule binding energy is used in the transfer process. This result opens the door for specialist functionals to be trained on different systems from little data, enhancing predictions while maintaining low training costs. Our approach of training a modified XC density functional approximation (DFA) furthermore allows for a highly interpretable result, as the neural network directly corresponds to a correction of the XC energy per electron.
The nuclear Schiff interaction (NSI) arises from a nuclear force that simultaneously violates spatial parity (P) and time reversal (T) symmetries, where T symmetry is equivalent to CP symmetry under CPT invariance. Detecting the NSI experimentally is important because CP violation is critical for explaining why the amount of matter in the Universe is far greater than that of antimatter. Measuring the NSI in molecules requires both precise experiments and theoretical calculations that incorporate electronic and nuclear wavefunctions. Conventionally, the electronic terms have been approximated using a first-order power series expansion of the electronic radial function-an approach that yields the well-known nuclear Schiff moment (NSM) -but this approximation may not be sufficiently accurate. In this study, we introduce a new, accurate analytical expression for the electronic terms based on Gaussian basis sets, which avoids any truncation of the power series. We find that the previous numerical approach overestimates the values for RaO and LrF by more than 50% and 300%, respectively, in the nuclear-radius region. In contrast to the numerical calculations, the analytical expression-based calculations show less sensitivity to choice of the basis-functions. Furthermore, we develop a new basis set that describes accurate behavior of wave functions both interior and exterior regions of nucleus. It also demonstrates that an even-tempered basis set is more preferrable over energy optimized basis set for calculating the NSI electronic term in molecules.
We study steady-state resonance light scattering from ensembles of noninteracting molecules, both in free space and inside optical cavities, while accounting for local thermal relaxation. The scattering spectra are obtained from steady-state solutions of either the Schr\"{o}dinger equation or a Liouville-space master equation. In the absence of a cavity, the spectra exhibit an elastic peak at the incident-photon energy and an inelastic fluorescence peak near the molecular excitation energy. Inside a cavity, the fluorescence peak splits into upper- and lower-polaritonic peaks in the strong-coupling regime. We analyze how the elastic and inelastic spectral features scale with the number of molecules under fixed cavity-molecule coupling and identify distinct collective trends in the Rayleigh peak intensity and in the integrated polaritonic or fluorescence spectral weight. The two theoretical approaches yield qualitatively consistent results while highlighting different aspects of thermally induced relaxation and dephasing.
A general polarizable embedded (PE) quantum mechanics/molecular mechanics scheme for periodic systems is presented, describing mutual polarization of the two subsystems. The QM system, described with density functional theory (DFT), is coupled to a single center multipole expansion (SCME) model, characterising H$_2$O molecules in the MM region. In SCME the H$_2$O molecules are ascribed anisotropic dipole and quadrupole polarizabilities and permanent multipoles up to and including the hexadecapole. Our embedding scheme illustrates a smooth and efficient convergence pattern of the periodic interaction potential by introducing a single and clustered multipole expansion points in the far-field. By choosing the near- and far-field expansion of the potential carefully the PE-QM/MM calculation matches the level of accuracy of a the QM calculation. In the short range, the electrostatic interaction between the QM and MM subsystems is damped with a real-space and pair-wise isotropic damping functions - resulting in a screened interaction and preventing over-polarization. In molecular dynamics simulations the two subsystems are separated with the elastic scattering assisted flexible inner region [Kirchhoff et. al. JCTC, 2021, 17, 9, 5863] - ensuring a smooth transition in the radial distribution at the boundary between the two subsystems.
Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retaining differentiability, a key property for materials design. Graph neural network (GNN)-based simulators have shown strong performance across a range of physical domains, including molecular dynamics. However, their reliance on temporal context for accurate prediction limits their use in inverse design settings, where simulations must be initialized from a single static configuration. Moreover, inverse design requires robust out-of-distribution (OOD) generalization, as candidate structures typically lie outside the training domain. Here, we address both challenges by introducing two complementary strategies that enable stable and accurate structure-only initialization of GNN-based simulations. To directly target OOD generalization, we propose an inference-time physics-based optimization framework that constrains model predictions to remain physically consistent during rollout. In addition, we introduce a differentiable, GNN-based barostat that enables accurate tracking of system dimensions and pressure, critical for capturing macroscopic responses and supporting OOD generalization. We evaluate these approaches in the context of uniaxial compression of disordered elastic networks spanning a broad range of geometries, Poisson ratios, and microscopic behaviors. We find that, together, these methods substantially improve rollout stability and enable reliable OOD generalization, including regimes with distinct, more complex dynamics than those in the training data. These results show that, when properly initialized and constrained, GNN-based simulators can serve as efficient and generalizable tools for materials discovery and structural optimization, advancing their use in materials, molecular, and dynamical system design.
A MACE potential trained on 50k configurations predicts reaction energies accurately on unseen high-index surfaces of bimetallic catalysts.
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Once trained, machine-learned interatomic potentials (MLIPs) provide a fast and accurate way to study catalytic reaction pathways, but their performance strongly depends on the training set. Here, we compare nine MLIPs trained with different data sets and strategies, including from-scratch (FS) training and fine-tuning (FT) of large foundation models. The models are evaluated on reaction energies, $E_{r}$, and reaction energy barriers, $E_{a}$, for 141 reactions, including CO$_2$ reduction to C$_2$ and C$_3$ products, propane dehydrogenation, hydrogen intercalation on Pd, and out-of-distribution oxygen evolution reaction (OER) on metal oxides. FS models trained with 5%--10% perturbed high-energy configurations from molecular dynamics or contour exploration reduce the error by more than twofold compared with models trained only on relaxation trajectories. In contrast, FT MLIPs are less sensitive to sampling and transfer well to out-of-distribution reactions. An MLIP fine-tuned on metallic catalysts achieves a 0.30 eV MAE for OER on iridium oxide polymorphs, outperforming out-of-the-box MACE-MH-1 by 0.08 eV and the best FS model by 0.14 eV. A model fine-tuned to O and OH adsorption on metal oxides gives a 0.19 eV reaction-barrier MAE for out-of-distribution CO$_2$RR on Cu, comparable to an FS model trained on in-distribution C--C bond-breaking reactions. Finally, a large MLIP fine-tuned on 49,860 configurations gives the best overall performance across metallic and metal-oxide catalysts and was used to screen a large left-out set of bimetallic alloys, achieving a 0.15 eV MAE for $E_{r}$, even for adsorbates on unseen Miller-index surfaces such as (532). This work identifies the training configurations needed for accurate FS and FT MLIPs for catalytic reaction modeling.
Interpretable ML ranks thermodynamic and geometric factors as top drivers of band gap in methane conversion catalysts, beating linear models
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The current catalyst discovery and development pipeline for energy-intensive applications like methane conversion remains bottlenecked by expensive trial-and-error experimentation, irreproducible chemical intuition, and a lack of frameworks linking complex catalytic design spaces to performance. This work presents an interpretable machine learning framework that integrates SHAP-based feature importance analysis (Explainable AI) with tree-based ensembles (Random Forest and Bayesian-optimized CatBoost) to characterize Fe-zeolite and oxide-supported catalysts for the partial oxidation of methane (POM). Despite limited data, the framework decodes complex structure-performance relationships by identifying and ranking thermodynamic, structural, and geometric microdescriptors that influence the electronic band gap and govern macroscale performance metrics such as selectivity, activity, and stability. This work explicitly demonstrates that thermodynamic lattice stability and geometric factors are the primary drivers of electronic band gap (a critical proxy for redox reactivity) rather than bulk stoichiometry. Non-linear models achieve an R2 of 0.61 - 0.77, significantly outperforming traditional linear baselines (R2 = 0.32). This workflow provides both a light-weight generalizable methodology and a prioritized list of physical features for accelerated catalyst screening - and these features can subsequently be integrated into microkinetic and reaction engineering models to create digital twins of complex reactor systems and to enable predictive optimization in autonomous R&D laboratories.
The homogeneous spectral linewidth associated with light-matter interactions is a fundamental descriptor of the optical properties of materials, governed by the quantum dynamics of the condensed-matter system. We discuss here that the homogeneous linewidth measured by means of two-dimensional electronic spectroscopy depends not only on microscopic coherence loss, but also on the observable through which the nonequilibrium dynamics are projected onto the measurement. In this Perspective, we develop a unified framework showing that changing the detection operator changes the operational definition of dephasing. For coherent emitted-field measurements, the observed linewidth largely retains its conventional connection to the optical coherence time $(T_2$). By contrast, in population-detected modalities such as photoluminescence-, photocurrent-, and other action-detected two-dimensional spectroscopies, the apparent linewidth can additionally encode excited-state population redistribution dynamics, leading naturally to an effective coherence time \(T_{2,\mathrm{eff}}\). Using a coupled-mode model propagated under a common Liouvillian, we show that identical microscopic dynamics yield distinct apparent dephasing times when projected onto coherent-emission and population-derived observables. We posit that the detection observable is not merely how a two-dimensional spectrum is measured, but part of what the spectrum fundamentally means as a materials probe.
Recent real-time TDDFT calculations have reported an unexpected delayed growth of molecular dipole oscillations some time after an extreme-ultraviolet (XUV) pulse is applied. We show that numerical and analytical arguments suggest that this instability is an artifact of an incorrect non-linearity introduced by the computational approach: Propagation with an adiabatic exchange-correlation approximation within the time-dependent Kohn-Sham equations of time-dependent density functional theory (TDDFT) tends to amplify initially small and pure sinusoidal oscillations in a system. On the other hand, when this same adiabatic approximation is used within the recent response-reformulated RR-TDDFT,the instability is absent. The absorbing boundary condition plays a crucial role consistent with our argument. We demonstrate this explicitly on the N2 molecule subject to an XUV pulse, with a range of adiabatic functionals.
The recently developed Doubles Connected Moments (DCM) expansion offers a tractable approach for computing correlation energy, exhibiting an noniterative O(N^6) scaling with system size N. Benchmark calculations on a set of molecules demonstrate that the DCM can outperform CCSD in terms of accuracy. To further enhance its efficiency, we present a stochastic variant of DCM by introducing a stochastic resolution-of-identity (sRI) technique, which decomposes the essential four-index intermediates. The resulting sRI-DCM scheme only involves one O(N^6) step, while all other steps do not exceed O(N^4) at each recursion, and reliably reproduces the results of conventional DCM. Our sRI-DCM achieves an overall experimental scaling of O(N^{4.46}) for series hydrogen dimer chains, demonstrating that it is attractive and practical for large systems containing hundreds of electrons.
Accurate calculation of free energies and their derivatives is central to assessing the thermodynamic stability of molecular and particulate systems across length scales. Yet such quantities can be difficult to compute reliably in strongly interacting systems, such as solutions of ionic species in polar solvents. One important example is the chemical potential of water in aqueous electrolytes, which can be estimated through staged particle insertion by gradually coupling an inserted molecule to its environment. Although the resulting insertion free energy should be independent of the alchemical pathway, the order and manner in which van der Waals and electrostatic interactions are activated can strongly affect convergence and, in some cases, yield inconsistent estimates. Here, we examine this issue by calculating water's chemical potential in aqueous KCl solutions using eight alchemical insertion pathways that differ in the extent and order of van der Waals and Coulombic coupling. We find that concurrently activating these interactions, particularly in fully coupled and partially end-coupled protocols, can produce chemically implausible insertion free energies. These anomalies arise from intermediate states in which the inserted water molecule develops strong electrostatic interactions with a chloride ion before sufficient short-range repulsion has been established. In contrast, pathways that activate short-range van der Waals interactions before electrostatics yield more consistent and chemically plausible estimates. These findings demonstrate that practical alchemical calculations in polar and ionic environments can be highly sensitive to pathway design, underscoring the importance of decoupling short-range and electrostatic interactions in staged insertion alchemical protocols.
Oriented external electric fields are ubiquitous in chemistry; however, the effects of fields applied in different directions on molecular systems remain underexplored. A major challenge is that an applied field exerts a torque on a molecule, reorienting the molecular frame and complicating the interpretation of orientation-dependent electric-field effects. Thus, free polar molecules experience orienting rather than oriented fields. In this work, we explore a new regime of distinct molecular equilibrium configurations, differing in the relative direction of the external field and the molecular frame, enabled by exploiting molecular polarizability rather than static dipole moment. These distinct "directomers" exhibit unique electronic and nuclear configurations, particularly in their low-lying excited states. We employ oriented electric field vectors referenced to a molecule-fixed principal axis frame along with hybrid analytical-numerical geometry optimization in order to explore the rotational potential energy surface (rRES), as well as a simply analytic model based on equilibrium electrical properties which captures the double-well character of the rPES, including some geometry relaxation effects.
Non-radiative decay in photoexcited molecular systems is driven by nuclear motion toward conical intersections (CIs), where electronic states become degenerate and nonadiabatic transitions occur. Identifying the nuclear degrees of freedom responsible for CI access from nonadiabatic molecular dynamics (NAD) simulations remains challenging because the underlying motions are high-dimensional and collective. Here, we introduce an unsupervised, information-theoretic framework based on Differentiable Information Imbalance (DII) to identify the nuclear coordinates governing CI access directly from trajectory surface hopping (TSH) simulations. By quantifying correlations between structural descriptors and electronic observables, including energy gaps and oscillator strengths, the method ranks nuclear degrees of freedom by predictive relevance. A multi-step protocol then extracts low-dimensional, physically interpretable modes associated with non-radiative decay. We apply the framework to the methaniminium cation, furan, L-glutamine, L-pyroglutamine-ammonium, and a photoactive molecular motor. Across all systems, the method recovers known mechanistic coordinates while revealing the relative importance of competing modes when multiple structural distortions contribute to CI access. The analysis also reveals a systematic distinction between observables: energy gaps are typically governed by a small number of localized coordinates, whereas oscillator strengths depend on more collective and distributed structural rearrangements. Overall, the DII-based framework combines predictive power with direct interpretability, providing a general and scalable route for extracting mechanistic insight from high-dimensional NAD data and constructing reduced-dimensional models of excited-state dynamics.
Single-Hessian Gaussian wavepacket dynamics (GWD) significantly reduces the computational burden of Heller's local harmonic GWD, while maintaining comparable accuracy in approximating vibronic spectra. Here, we provide a new, symplectic derivation of the equations of motion of single-Hessian GWD and show that, unlike the local harmonic version, this method conserves the non-canonical symplectic structure on the manifold of Gaussian wavepackets and$-$for bounded dynamics in smooth potentials$-$avoids the drift of energy. Our numerical results suggest that, despite being much more efficient than the local harmonic variant, the single-Hessian GWD exhibits the same $\mathcal{O}(\hbar)$ asymptotic error in averages of observables. To further accelerate numerical simulations, we implement high-order time-stepping geometric integrators that are time-reversible and conserve the norm and symplectic structure exactly, regardless of the time step. In addition, we present explicit expressions for the exact evolution of the width of a single-Hessian Gaussian wavepacket in a general potential, as well as for the exact evolution of the whole wavepacket in a global harmonic potential. Using on-the-fly ab initio Gaussian wavepacket dynamics on the first excited-state surface of ammonia, we numerically confirm the conservation of geometric properties by these integrators and demonstrate that high-order integrators can enhance both accuracy and computational efficiency. We also compute the photoelectron spectrum of the difluorocarbene anion and the absorption spectrum of methylamine, and find that, in comparison with experiment, single-Hessian GWD outperforms global harmonic models and matches the accuracy of local harmonic GWD. Finally, we identify which spectral features are sensitive to the choice of reference Hessian.
Positron binding energies and Dyson orbitals for five-membered heterocycles with N, O, S and NH substituents are predicted \emph{ab initio} via many-body theory. The positron-molecule correlation potential (self energy) is calculated via solution of Bethe-Salpeter equations that describe the positron-induced polarization of the target and screening of the electron-positron Coulomb interaction at the $GW$@BSE level, the infinite electron-positron ladder series that describes the crucially important process of virtual positronium formation, and the analogous positron-hole ladder series. The all-order calculations employ Gaussian-orbital bases and are implemented in the {\tt EXCITON+} code. The effect of substituting combinations of N, O and S atoms, and the NH group in the molecule's ring is studied, and the role of individual molecular orbitals, many of which are found to significantly contribute to the correlation potential, quantified. Analysis of the positron bound-state Dyson orbitals shows that the positron is typically localized next to one or two of the substituents in the ring, with the order of preference N, S, O, then NH, and is also influenced by aromaticity and the presence of double ($\pi$) bonds in the ring.
Density fitting plus reduced basis and approximate Z-vector keep errors negligible while finishing each step in under a minute for 73-atom 3
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The electronic structure calculations remain a major bottleneck in ab initio nonadiabatic molecular dynamics. We develop an efficient TDDFT-based FSSH implementation in the GPU4PySCF package for medium-sized molecular systems. Our approach combines density fitting, TDDFT with minimal auxiliary basis sets (TDDFT-ris), and an approximate Z-vector solver to reduce the computational cost of TDDFT excited states and derivative coupling calculations. These approximations introduce negligible errors in realistic FSSH workloads while maintaining high computational efficiency. Benchmark results show that, for 73-atom systems with a triple-$\zeta$ basis set, individual electronic structure calculations are completed within one minute on a single NVIDIA A100 GPU.
Optimizations cut latency by 75.58% and boost efficiency to 10.11 GOPS/W with 52% less resources.
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Computational complexity and storage requirements are crucial factors influencing the performance and efficiency of convolutional neural networks (CNNs) in resource-constrained environments. This paper presents a high-performance embedded target detection system based on FPGA and YOLOv3-Tiny, specifically designed for embedded artificial intelligence applications. By integrating lightweight CNN optimization techniques with hardware accelerator design, significant improvements are made in both computational efficiency and resource utilization. Key optimizations, including low-bit quantization, batch normalization fusion, and table lookup mapping, reduce model parameters and computational complexity. Additionally, an FPGA hardware accelerator with a pipelined architecture is developed to enhance the efficiency of convolution operations while minimizing off-chip data transmission through modular design and on-chip cache optimization. On the ZYNQ-XC7Z035 platform, the system achieves an inference latency of 0.211 seconds, outperforming comparable designs by 75.58% in speed. The system achieves an power efficiency of 10.11 GOPS/W, surpassing comparable designs by at least 29.45%. Furthermore, hardware resource utilization is reduced by up to 51.94% compared to similar systems. This study offers innovative design methodologies and practical application examples for the efficient deployment of deep learning models on embedded platforms.
FunctionalAgent links data prep, calculations, and fitting into one loop, yielding MC26 and COF26 that outperform prior methods on the same
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Multiconfiguration pair-density functional theory (MC-PDFT) offers an efficient and accurate framework for computing electronic energies in strongly correlated molecular systems, with the quality of the on-top functional being a key determinant of its predictive accuracy. Here we introduce FunctionalAgent, an agentic system for fully automated functional development. FunctionalAgent orchestrates a team of specialized sub-agents to decompose the development process into dataset construction, active-space generation, MCSCF calculation and descriptor generation, loss-function construction, and functional fitting, optimization, and evaluation, thereby linking all stages into a closed-loop automated workflow. Using FunctionalAgent, we developed MC26, a hybrid meta-GGA on-top functional that achieves improved overall accuracy on the training set compared with other methods evaluated on the same benchmark dataset. We further introduce COF26, a new functional form that, owing to the optimized training process, achieves the best performance on both the training and test sets.
Accurate prediction of adiabatic $0$-$0$ excited-state energies is crucial for modeling molecular photophysical processes. Here, we benchmark computational strategies for evaluating excited-state energies and singlet-triplet gaps obtained using different geometry-optimization strategies, including time-dependent density functional theory (TDDFT), spin-unrestricted Kohn-Sham (UKS) DFT for triplet states (${\rm T}_1$), and state-specific orbital-optimized UKS (ssUKS) DFT for singlet excited states (${\rm S}_1$). Zero-point vibrational energy corrections are evaluated consistently at the optimized geometries and combined with ADC(2) excitation energies for comparison with experimental anion photoelectron spectroscopy data for a representative set of molecules. Among the protocols considered, adiabatic $0$-$0$ energies evaluated at TDDFT-optimized ${\rm S}_1$ and ${\rm T}_1$ geometries show the best agreement with experiment, with a mean absolute error below 0.1 eV. Replacing these geometries with UKS-optimized ${\rm T}_1$ and ssUKS-optimized ${\rm S}_1$ structures yields comparable accuracy. Vertical excitation energies are substantially more sensitive to the choice of geometry than the corresponding ${\rm S}_1$-${\rm T}_1$ gaps, which are comparatively more robust because of partial error cancellation. As a larger case study, we examine rubrene and find that UKS/ssUKS-based geometries remain useful for evaluating singlet-fission energetics. Overall, UKS/ssUKS-based workflows provide an efficient and accurate route to excited-state geometry optimization and to the evaluation of adiabatic $0$-$0$ energies for states with dominant single-determinant character.
Recent reports suggest that chemical reaction rates can change when reactants are placed inside an optical cavity. These effects have been attributed to the hybridization of molecular vibrational modes with cavity modes into polaritons, but the underlying mechanism remains debated. Recently, attempts to reproduce the key experiments have sometimes failed, which poses also ambiguity and impedes the determination of the possible mechanism. Without a reliable theoretical framework, polaritonic chemistry -- which seeks to use optical resonators as catalysts to control reactions -- has reached a pivotal stage. Standardized protocols for reproducible cavity experiments are therefore urgently needed. Here, we identify pitfalls in approaches that monitor reaction progress with UV/Vis spectroscopy. Using the Transfer Matrix Method, we analyze a model pseudo-first-order reaction and assess how transient cavity thickness variations, cavity inhomogeneity, and fitting protocols influence the extracted rate constant. We find that changes in cavity thickness upon reactant introduction can strongly distort apparent kinetics when monitoring at a single wavelength, an artifact that can be mitigated by spectral smoothing. Additionally, we demonstrate that, unlike in many previous studies, the asymptotic extinction should be treated as a fitting parameter rather than fixed to the final experimental value. By identifying these pitfalls, our work lays the foundation for more robust analyses and reliable measurements in polaritonic chemistry.
Machine learning interatomic potentials (MLIPs) offer first-principles accuracy with reduced computational cost, but their transferability across different thermodynamic states remains questionable, particularly for fluid systems where molecules experience local environments far from crystalline equilibrium. Here, we demonstrate that diversifying the density of training configurations, rather than temperature, is the most effective strategy for building thermodynamically transferable MLIPs within a fixed computational budget. We first show that foundation MLIPs trained on solid-state databases accurately describe liquid-like densities but fail at gas-like conditions, while molecular-database-trained models exhibit the opposite behavior. Controlled from-scratch training and distillation experiments confirm that density-diverse datasets resolve both failure modes, whereas temperature-diverse datasets cannot compensate for missing density regimes. Coordination number analysis reveals the physical origin of this behavior: local coordination topology is more susceptible to density than temperature, leading to further structural diversity. These results establish density diversity as a design principle for thermodynamically transferable MLIPs and provide a validation framework for assessing the thermodynamic coverage of both foundation and from-scratch models, enabling reliable atomistic simulation of fluid-phase processes across diverse operating conditions.
Iridium-based photosensitizers have attracted significant attention in photodynamic therapy (PDT) due to their exceptional photophysical properties and chemical stability, as well as tunable phosphorescence emission spectrum and high triplet state production yields. Photosensitizers with large two-photon absorption (TPA) and mitochondrial targeting capabilities are particularly promising for clinical PDT, as they enable deeper tissue penetration and reduced damage to normal cells. In this study, we theoretically studied photophysical, photodynamic properties and photosensitization reaction mechanism of a series of iridium-based photosensitizers with modified C^N and N^N ligands (a2-a6, b1/b1-r and b2/b2-r) by TDDFT/DFT methods. The photophysical properties, including one- and two-photon absorption spectra, frontier molecular orbitals, and singlet and triplet excitation energies, were calculated. Additionally, rate constants for intersystem crossing, fluorescence, and phosphorescence, along with water solubility and lipophilicity metrics (logP), were determined to assess both efficacy and biocompatibility. The results elucidate the modulation roles of the chelated ligands and ancillary ligands in TP-PDT efficiency, indicating that the asymmetric iso-fused-benzene ring modification to the N^N ligand is a robust design strategy for comprehensively enhancing photosensitization performance. Complexes a2, b2 and b1-r show greater promise as candidates for two-photon PDT photosensitizers, owing to their large TPA cross-sections, extended triplet state lifetimes, and balanced water solubility and lipophilicity. Notably, the b1-r complex can undergo both Type I and Type II PDT photosensitization mechanisms, which will help address the issue of drug resistance arising from the hypoxic environment in deep-seated tumors.
Treating the solvent as an ideal polar fluid with correlated quantum states extends Marcus theory to fast collective motion and backreaction
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We propose a mixed quantum-classical hydrodynamic framework to model short-time inertial effects in the non-adiabatic evolution of a quantum solute coupled to a classical polar solvent. Drawing upon the work of Burghardt and Bagchi [Chem. Phys. 329 (2006), 343], we employ the Hamiltonian approach to incorporate consistent backreaction and preserve quantum decoherence beyond standard Ehrenfest dynamics. The solvent is treated as an ideal polar fluid and the quantum solute state is correlated to both the position and molecular orientation coordinates of the liquid. This approach retains essential solute-solvent correlations while significantly reducing the computational complexity of previous approaches. We further incorporate dissipative terms to capture both inertial effects and polarization relaxation. After establishing the general setting for non-local dielectric continua, the Marcus local approximation is integrated into the model thereby extending traditional solvation theory to account for collective fluid sloshing on fast timescales.
Symmetry-constrained fitting yields cross-sections showing strong CH radical production in non-adiabatic fragmentation channels.
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Tracking the complex non-adiabatic transitions in far-ultraviolet photodissociation demands highly accurate
diabatic potential energy matrices (PEMs) across numerous excited states. To address this, we introduce a fully automated
diabatization method that leverages artificial neural networks to fit PEMs.
Our approach divides the PEM into a physically grounded zeroth-order diagonal term, which is then corrected by a neural network matrix to capture electronic couplings.
By enforcing symmetry constraints on off-diagonal elements and sharing degenerate diabatic states between the $A'$ and $A''$ irreducible representations,
the { diabatization} process becomes completely automatic.
We validate this method using time-dependent wavepacket calculations to simulate the photodissociation of CH$_2^+$, incorporating relevant states up to $\approx 13.6$~eV.
Finally, we compute partial cross-sections for all fragmentation channels -- including total and partial fragmentation yielding
\ce{CH+}, \ce{CH}, \ce{H2}, and \ce{H2+} diatoms -- revealing a notably high cross-section for the formation of the \ce{CH} radical.
While human technology is ruled by determinism, biological systems exploit a subtle balance of control and stochasticity. This balance, evident in the morphogenesis of textural patterns imprinted on leaves, fur or skin can help hierarchize organisms both as a representative of their species and as unique individuals. In this study, we identified that, by exploiting electrochemistry, it is possible to generate such versatile but specific textures, to imprint patterns of a conducting polymer on a conducting substrate. It is shown that the 1D morphogenesis of conducting polymer dendrites on wires translates, on 2D surfaces, as highly heterogeneous coatings of dark spots, rosettes or marbled patterns. Despite their inherent stochasticity, these patterns are characteristic of the physical conditions they grew in, and particularly of the chemical content of the electroactive solution used for their electropolymerization. A statistical study demonstrates that these patterns could be used as fingerprints to physically tag the identity of a solution within a specific class. By the identification of a new electrochemical process which allows generating physical fingerprints with optical, electrical and chemical contrast on an electrode, this research paves the way toward a disruptive low-cost technology which could allow any end-user to generate personal tags on a glass slide or on a micro-chip, to engrave physically-encrypted personal information for various applications.
Gausslets are one of the few basis constructions for electronic structure that combine locality, orthonormality, variable resolution, and an accurate diagonal approximation for the electron-electron interaction, but the original construction is tied to one dimension. Radial gausslets extended this idea to atoms while leaving the angular degrees of freedom in spherical harmonics, so the atomic interaction remained only partially diagonal in the combined basis. Here we introduce generalized gausslets on the sphere and combine them shell by shell with radial gausslets to form an atom-centered basis in which the electron-electron interaction takes a two-index integral-diagonal form. The angular basis starts from localized spherical Gaussians and uses injection to make a low-$\ell$ spherical-harmonic subspace exact. Tests of the kinetic spectrum, low-$\ell$ Coulomb matrix elements, spherium, first-row Hartree--Fock calculations, and He exact diagonalization show systematic convergence with increasing angular resolution. We also develop DMRG methods for this basis, including compact MPOs, correlated small-space starting states, Givens-rotation transfers between nearby angular sizes, and embedded sampled variance extrapolation (ESVE). We show that this combination of ingredients can be used to solve the Be atom, with extrapolations in the number of angular functions but with fixed radial resolution, to within about 0.1 mH of the complete basis set limit exact energy. This shows that DMRG calculations of first row atoms which include both static and accurate dynamic correlation on the same footing are feasible.
Machine-learned interatomic potentials (MLPs) provide near density functional theory (DFT) accuracy at reduced computational cost, but their reliability depends on representative training data and often deteriorates in transition-state regions governing rare events. We introduce an active-learning framework in which Transition Path Sampling (TPS) serves as a targeted data-generation engine for constructing MLPs accurate in barrier regions. TPS generates ensembles of unbiased reactive trajectories, and a committee-based uncertainty estimate identifies configurations for selective DFT labeling and retraining. Iterating this cycle systematically refines the potential energy surface in dynamically relevant regions, without the need of prior knowledge of the mechanism. Applied to electrochemical CO$_2$ reduction to CO on copper in explicit water, the approach removes nonphysical artifacts present in early models, achieves near-DFT energy and force accuracy, and enables stable long-time sampling of reactive pathways. Extended TPS simulations reveal multiple dynamically accessible protonation mechanisms. This work establishes TPS as an efficient and principled active-learning strategy for reactive molecular simulations at electrochemical interfaces.
Phaseless auxiliary-field quantum Monte Carlo (ph-AFQMC) has emerged as a promising electronic structure method for correlated electronic systems. However, the quality of its predictions depends critically on the choice of trial wavefunction, and it is not obvious how to make an optimal choice especially for strongly correlated states of large systems. Mean-field wavefunctions are compelling trial wavefunction candidates as they map directly to chemical concepts and can be obtained with $O(N^4)$ cost. Yet in the strongly correlated regime one faces a symmetry dilemma and the existence of multiple nearly-degenerate solutions. In this work we investigate active space models of [2Fe-2S]$^{2+}$, mixed-valent [4Fe-4S]$^{2+}$, and [4Fe-4S]$^{4+}$ and explore the sensitivity of ph-AFQMC to the choice of unrestricted Hartree-Fock trial wavefunction. We find that chemical properties and physical symmetries, rather than the variational energy, ought to guide the choice of mean-field trial for ph-AFQMC (or reference state for coupled cluster models), and show that surprisingly accurate ground-state energies for these systems can be obtained. However, in all cases we find a rapidly vanishing overlap between the stochastic wavefunction and the UHF trial, indicating that the trials are suboptimal importance functions. By analogy to a similar situation in the stretched helium dimer cation, we show how this sampling bias pushes ph-AFQMC towards artificially negative energies, which evidently can be compensated for by the phaseless bias in certain cases.
Phaseless auxiliary-field quantum Monte Carlo (AFQMC) has in several cases been found to perform well on strongly correlated systems. Here, we benchmark the method for three iron-sulfur clusters ([2Fe-2S], [4Fe-4S], and the FeMo cofactor) using a hierarchy of trial states derived from coupled cluster (CC) theory, including up to quadruple excitations, as well as multi-Slater trial states derived from the density matrix renormalization group. Our results reveal for these systems that, as the symmetry-broken trial is improved, the phaseless AFQMC energy can become less accurate, and in some cases even less accurate than the underlying trial projected energy, displaying an inverted energy pattern that is only corrected once the trial fidelity is sufficiently high. For [2Fe-2S], we show that this can coincide with a simultaneous improvement in the trial state and the walker ensemble. We further find that this is not solely due to the use of spin-unrestricted trial states, as the inversion persists in [2Fe-2S] when we explicitly break the symmetry of the Hamiltonian by applying a fictitious spin-Zeeman field. Instead, we find that the energy inversion is related to the choice of measurement trial, where using a high-order CC trial state for measurements may introduce errors that are suppressed when the measurement wave function is restricted to lower excitation subspaces. In particular, measuring the energy with the mean-field reference while guiding the walkers with a CC trial improves the overall accuracy across the iron-sulfur clusters, with a possible exception for [4Fe-4S]. Taken together, our findings suggest that the relatively accurate energies obtained with an HF trial state in these systems arise from favorable error cancellation, warranting significant caution about the reliability of phaseless AFQMC with such trials for strongly correlated transition-metal systems of this kind.
An 11-species kinetic treatment reproduces measured profiles and induction zones in H2-He entry flows better than prior models.
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An 11-species thermochemical model for H$_2$/ He mixtures with state-specific kinetics for atomic H is developed and used to simulate 1-D shocks at conditions relevant for ice and gas giant entry flows. To implement this kinetic model, a literature review of the state-specific excitation and ionization rate constants of atomic H is first performed. While electron-impact rate constants from various sources are found to be in good agreement, large discrepancies are found in the limited data available on heavy-particle-impact rate constants. To validate the kinetic model, 1-D steady shocks are simulated using a space-marching code that explicitly accounts for shock tube boundary layer effects. The resulting radiance profiles are compared to experimental data from the NASA Ames Electric Arc Shock Tube (EAST) facility, and are found to reproduce the measured values reasonably accurately while capturing the distinct induction zone behavior observed in the experiments. A sensitivity analysis of the kinetic rates and boundary layer treatment reveals avenues for further improvement of the model. Finally, a comparison to alternate models from the literature underscores the improved accuracy of the present model in predicting ionization and radiation profiles.
Excited-state electronic structure in strongly correlated systems remains challenging due to the exponential scaling of the many-body Hilbert space and the difficulty of constructing systematically controlled active spaces. Building on the stochastic cluster expansion (SCE) framework previously developed for ground-state correlation energies, we extend the formalism to excitation gaps by expressing energy differences directly as a hierarchy of orbital-space cluster contributions. In this formulation, excitation energies are reconstructed from reduced-rank calculations involving a minimal frontier chemical subspace (FCS), treated exactly, together with stochastic sampling of the remaining orbital environment. This approach eliminates the need for large or chemically preselected active spaces. We demonstrate the method on charge-transfer complexes and polyacenes, where accurate singlet-triplet gaps are obtained that agree with full-system results. The method converges with low-order cluster terms and provides a systematically improvable framework for excited states in correlated systems.
We assess the performance of the Quantum Flow (QFlow) algorithm employing cost-effective solvers based on the unitary coupled-cluster ansatz with single and double excitations (QFlow-SD). The resulting energies are benchmarked against those obtained with an analogous QFlow formulation defined in the same active spaces but augmented by higher-rank excitations, including triples and quadruples (QFlow-SDTQ). Across all molecular systems considered, QFlow-SD exhibits close agreement with results from the canonical unitary coupled cluster with singles and doubles framework, while requiring substantially fewer qubits than the latter. For the water molecule in the cc-pVTZ basis, we further demonstrate the performance of a composite two-step downfolding strategy. In this approach, an initial coupled-cluster downfolding based on the double unitary coupled-cluster ansatz is followed by a QFlow treatment within the resulting target space, illustrating the effectiveness of combining classical downfolding with quantum flow optimization.
Asymmetric valences in a binary electrolyte can significantly affect the performance of systems such as reverse electrodialysis cells, batteries, and supercapacitors. To generate a theoretical understanding of this effect, we consider a steady one-dimensional Poisson-Nernst-Planck model of an electrolytic cell with imposed constant ionic fluxes, focusing on varying ion valences in a general asymmetric binary electrolyte. Numerical simulations reveal a smooth transition between the qualitatively distinct near-equilibrium and strongly non-equilibrium steady-state regimes. These regimes are distinguished by a valence-dependent transition point at an intermediate current where the classical Debye-scale boundary layer vanishes. We characterise this transition using asymptotic analysis, recovering the Gouy-Chapman and limiting-current results in the appropriate limits, and determining the correct transition results when neither is appropriate. We provide implicit solutions for the potential and ion concentrations of general asymmetric binary electrolytes and, notably, we provide explicit analytic expressions for the asymptotic composite solutions for 2z:z, z:2z, and z:z electrolytes. We show how the results can be presented in a collapsed phase diagram that can be used to predict qualitative intermediate-current steady-state behaviour in terms of ion valences and fluxes.
Tree tensor network states (TTNSs) combined with the density matrix renormalization group (DMRG) are emerging as powerful tools for vibrational and vibronic structure simulations in molecules with strong coupling and fluxionality. In this Perspective, we discuss how TTNS methods enable accurate, full-dimensional computations of thousands of eigenstates for molecular systems ranging from quartic-force-field benchmarks to molecules with strong vibronic coupling and protonated water clusters as large as the 33-dimensional Eigen ion, H$_3$O$^+$$\cdot$(H$_2$O)$_3$. We emphasize the close connection and interoperability between DMRG-based TTNS methods and the multilayer multiconfiguration time-dependent Hartree method (ML-MCTDH), which share the same underlying ansatz. We also highlight practical challenges of predictive simulations, including robust error estimation, convergence of observables such as infrared intensities, and optimization of tensor network tree structures. Finally, we outline recent advances toward direct targeting of excited states and discuss opportunities for broader applications in molecular spectroscopy and quantum dynamics.
In this work, we report on the high-resolution infrared spectrum of CCH$^+$ ($^3\Pi$) recorded in the range $3066-3184$~cm$^{-1}$ by means of leak-out spectroscopy. This spectral range covers the fundamental of the CH stretching mode and a highly excited bending vibrational mode. Based on this data (385 ro-vibrational lines), accurate spectroscopic descriptions of the ground and the two vibrationally excited states of CCH$^+$ were obtained. Besides the band origins, spin-orbit coupling constants, rotational constants, centrifugal distortion constants and $\Lambda$-doubling constants for the ground and excited vibrational states have been derived. This effective Hamiltonian analysis allowed a search for pure rotational lines of CCH$^+$ in its electronic and vibrational ground state using a two-color millimeterwave - infrared scheme. We observed all rotational transitions from $J^{\prime\prime} = 2$ up to $J^{\prime\prime} = 6$ within the $\Omega = 2$ lowest energy fine structure component with resolved hyperfine splittings. This data has already guided the first detection of CCH$^+$ in space toward the Orion Bar photo-dissociation region, and has the potential to support further astronomical searches for CCH$^+$ either through radio or infrared spectroscopy, for example with the James Webb Space Telescope.
We present a class and home problem in graduate transport phenomena and electrochemical engineering that clarifies a common misconception: electroneutrality implies the electric field is constant. Starting with one-dimensional Poisson--Nernst--Planck equations for a silver electroplating cell, students obtain concentration and potential profiles. A companion home problem with added background electrolyte introduces a new dimensionless ratio and admits a closed-form solution. Students conclude that electroneutrality is necessary but not sufficient for a constant electric field.
The Pauli principle has far-reaching consequences in quantum physics. Here, we investigate, for the first time, its implications, together with nuclear spin isomerism, in polaritonic chemistry. We first present an accurate numerical description in a realistic situation of two $^{14}$NH$_3$ molecules, existing as ortho and para spin isomers, in an infrared cavity. Then, we generalize these results using an analytical model for molecular ensembles. Our findings undoubtedly demonstrate that the Pauli principle and nuclear spin isomerism significantly reshape collective light-matter coupling.
We present an efficient implementation of the second-order two-component relativistic core-valence-separated algebraic diagrammatic construction method (CVS-ADC(2)) for core-excitation calculations. The approach employs state-averaged frozen natural spinors (SA-FNS) to reduce the number of floating-point operations, together with the Cholesky decomposition (CD) technique, which lowers the storage requirements associated with two-electron integrals. These reductions make the method particularly well-suited for systems containing heavy elements. Systematic benchmarking against four-component reference calculations confirms the reliability and robustness of the two-component (X2CMP/X2CAMF)-based framework. The close agreement with canonical results further demonstrates that the SA-FNS-based CVS-ADC(2) approach achieves comparable accuracy at only a fraction of the computational cost. Moreover, benchmark studies of L$_{2,3}$-edge spectra for 3$d$ transition-metal compounds demonstrate that CVS-ADC(2) serves as a computationally efficient and reliable alternative to the non-Hermitian EOM-CC method for reproducing experimental spectra. Finally, calculations on a ruthenium complex illustrate the method's applicability to relativistic studies of medium-sized molecular systems.
We present \textsc{dm-PhiSNet}, a physically constrained \textsc{PhiSNet}-based equivariant model that predicts one-electron reduced density matrices (1-RDMs) directly from molecular geometries in an atomic-orbital (AO) basis for accelerated self-consistent field (SCF) workflows. Training follows a two-stage schedule with progressively introduced physically motivated objectives, and the resulting predictions are refined by a lightweight analytic block. This block enforces electron-number conservation, drives the 1-RDM toward generalized idempotency in the AO metric, and regularizes the occupation spectrum of the L\"owdin-orthogonalized density. Across six closed-shell systems -- H$_2$O, CH$_4$, NH$_3$, HF, ethanol, and NO$_3^-$ -- the refined 1-RDMs provide SCF initial guesses that substantially reduce iteration steps by 49--81\% relative to standard initializations. Beyond SCF acceleration, the learned 1-RDMs yield accurate one-shot total energies and Hellmann--Feynman atomic forces without force supervision, indicating that the model captures chemically meaningful electronic structure. These results demonstrate that combining equivariant learning with analytic constraint enforcement provides a simple, general route to solver-ready density-matrix initializations and accelerated SCF workflows.
The chemical bond is a central organizing concept in chemistry, yet it is absent from the molecular Hamiltonian and no "bond operator" exists. Bonding is therefore not a primitive physical entity but a derived descriptor emerging from the quantum state. The logical consequences of this observation are revisited. Statements such as "bonding stabilizes structure" when taken literally risk circular reasoning (petitio principii), whereby bonding is inferred from a stationary structure and then invoked as its cause. The same caution applies to concepts such as steric repulsion, which is also a derived descriptor. Bonding accompanies stable or metastable states and correlates with their properties without constituting their cause. Illustrative examples are drawn from QTAIM, non-covalent interaction (NCI) approach, protein structure, and hydrogen-hydrogen bonding. Causation, language, and the autonomy of chemistry are also briefly discussed. The aim is not at all to diminish the role of bonding, but to place it at the correct logical level, that is, as a powerful, state-dependent descriptor that organizes, classifies, and predicts chemical behavior without serving as its fundamental cause.
Recent advances in occupancy extrapolation (OE) show that potential of orbital-occupation based energy functions can describe electronic excitations. Here, the OE method in the particle-hole channel is extended to an effective quasiparticle Hamiltonian, enabling a multi-configurational description beyond single-determinant OE and $\Delta$SCF. The method performs comparably to the Bethe-Salpeter equation for valence singlet and charge-transfer excitations, and better for valence triplet and Rydberg states, supporting its accuracy and broad applicability.
Several recent methods have shown that it is possible to compute rate constants of very slow biomolecular processes using simulations where a time-dependent bias is added along one or several collective variables (CVs). We previously reported the exponential average time-dependent rate (EATR) method, which can improve upon these approaches by accounting for how efficiently the external biasing potential modifies the observed rate using a learned CV-quality factor $\gamma$. This results in more accurate rate estimates using the same data when biasing a suboptimal coordinate. However, as formulated EATR depended on the biasing potential varying over time to properly determine the biasing efficiency, which limits the method's applicability to quasi-static biasing schemes such as ``flooding'' or on-the-fly probability enhanced sampling (OPES). Here, we present the EATR-flooding approach, which generalizes our method by replacing the need for a time dependent bias by instead varying (stepping up) the strength of the biasing potential across multiple sets of simulations. We implement this approach as an open-source Python library, and demonstrate that this approach is accurate without substantial loss of efficiency compared to standard EATR for a coarse-grained protein system, and also show good performance on a fully atomistic cavity-ligand model. Two additional appealing features of EATR-flooding are an internal check for over-biasing and the fact that only a single $\gamma$ parameter is predicted for a given choice of CVs, as compared to our earlier results where $\gamma$ empirically depended on biasing rate. Finally, we believe EATR-flooding applies not only to OPES simulations but more generally to CV biasing enhanced sampling approaches, making it broadly useful.
Graph neural network architectures are advantageous for predicting core-electron binding energies which depend on local bond environment effects, as the number of message passing layers defines the topological (bond) radius of the model's receptive field. This provides an interpretable connection between the model's architecture and the definition of locality in the considered environment. Here we present a graph neural network model for predicting carbon 1s core-electron binding energies in organic molecules. The model is trained with multiconfiguration pair-density functional theory on 8637 carbon atoms in 2116 molecules with 4-16 atoms and evaluated against 570 experimental values in 113 different molecules containing 3-45 atoms. Previous work benchmarked a mean absolute error of 0.27 eV to experiment for the training data level of theory [J. Phys. Chem. A 2025, 129, 36, 8419-8431] and the present model demonstrates an experimental evaluation error of 0.33 eV with good size transferability to larger systems. By examining the effect of the number of message passing layers on the performance, we show that two chemically informed node features, the atomic binding energy and environment electronegativity, encode molecule-specific information when normalized across the graph and capture beyond nearest-neighbor environment effects outside the receptive field. A case study on the 45 atom avobenzone tautomers demonstrates the model's ability for instant and precise analysis of complex molecules. Finally, the model's E(3)-equivariance is shown to out-perform an invariant model on non-equilibrium geometries from a methanol C-O bond stretch. The software and data are provided by the open-source AugerNet package at https://doi.org/10.5281/zenodo.19689244.
The transition from gaseous precursors to nascent solid particles and their subsequent structural maturation were investigated in single-pulse shock-tube experiments using ex situ Fourier-transform infrared (FTIR) and Raman spectroscopy of sampled products. A mixture of 2% toluene in argon was pyrolyzed at around 2.0 bar with temperature plateau times of 2.0 ms over the 1450-1800 K reaction temperature range. In situ laser extinction measurements indicate the onset of particle formation at 1570 K. At this temperature, Raman spectra exhibit emerging D and G bands, and transmission electron microscopy (TEM) reveals the disappearance of poorly defined structures, identifying 1570 K as the phase-transition reaction temperature. Approaching this reaction temperature, Raman spectra show a rapid disappearance of sp hybridized triple carbon bonds. At 1670 K reaction temperature, a maximum in primary particle diameter and a decrease in structural disorder inferred from Raman spectroscopy are observed, defining the ordering threshold. Deconvolution of the FTIR spectra enables separation of in ring double carbon bond stretching vibrations from isolated and ring-conjugated side-chain double carbon bond modes. The in-ring double carbon band is used to normalize aliphatic and aromatic C-H vibrations. FTIR analysis reveals ring-edge structures associated with electron-localization sites, including bay regions, five-membered ring defects, and benzylic positions, indicating a radical-rich environment below the phase-transition temperature. Between the phase-transition and ordering-threshold temperatures, K-regions and armchair structures associated with electron delocalization and thermal stability increase. The emergence of these electronic and structural characteristics highlights the critical role of radicals in soot inception and early structural ordering.
Higher energy density and longer lifetime are the requirements for next-generation lithium-ion batteries. A promising anode material is silicon, which offers high specific capacity, but its significant volume change during lithiation and delithiation enormously reduces battery lifetime. A physical understanding of the processes degrading the battery is key to mitigate this effect and advance in the field. We develop a physics-based model to describe degradation during battery cycling under various protocols and storage conditions, with varying check-up (CU) frequencies. The model can disentangle basic degradation mechanisms, such as the growth of the Solid-Electrolyte Interphase (SEI), from silicon mechanisms, such as particle cracking, SEI growth on cracks, and loss of active material (LAM). We investigate the impact of CUs on the observed storage degradation and the reason behind the increased degradation in batteries, including silicon in the anode. Additionally, we relate the observed degradation to operating conditions, enabling future optimization of battery use and design.
Topological indices are numerical invariants derived from molecular graphs and play an important role in characterizing chemical compounds and predicting their properties. Among the earliest descriptors are the classical Zagreb indices introduced by Gutman and Trinajsti\'c in 1972. A more recent development is the hyper-Zagreb index ($HM$), defined as $HM(G)=\sum_{v_i v_j\in E(G)}(d_i+d_j)^2$, where $d_i$ denotes the degree of vertex $v_i$. In 2023, Hayat et al. posed an open problem concerning bounds on the $HM$ index under fixed vertex-connectivity or edge-connectivity, along with the characterization of the corresponding extremal graphs. In this work, the problem is resolved by determining the extremal graphs that maximize $HM$ index under these constraints. The investigation is further extended to several additional extremal problems, including graphs with a given number of leaves, chromatic number, and independence number. The associated extremal graphs are identified in each case. In addition, the chemical relevance of $HM$ is examined through QSPR studies. Finally, the conclusion is presented.
SZ-QCT relaxes the generator-size limit in seniority-zero mappings while keeping the same O(N^8) scaling.
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We propose a method to solve the Schr\"odinger equation for systems with static/strong electron correlation using Hamiltonian transformations. Building on our previous work on seniority-zero canonical transformation theory, which seeks a unitary transformation that maps the Hamiltonian into the seniority-zero space, this method presents an alternative way of evaluating the Baker--Campbell--Hausdorff (BCH) expansion based on quadratic canonical transformation theory. The extension aims to relax the small-generator constraint by allowing approximate four-body contributions in the expansion, thus expanding the class of excitations previously allowed in SZ-LCT, where only approximate three-body operators were retained. Numerical tests reveal that the seniority-zero quadratic canonical transformation method (SZ-QCT) delivers good accuracy, with most errors within chemical accuracy. In particular, SZ-QCT shows sub-millihartree errors in cases where larger generators are needed to recover the residual dynamic correlation. The computational scaling of SZ-QCT is the same as that of SZ-LCT, $\mathcal{O}(N^8/n_c)$, where $n_c$ is the number of cores available for the computation
Solv-eze places explicit waters via 3D-RISM high-probability regions, reproducing many crystallographic bridging waters in protein-ligand…
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Molecular dynamics (MD) simulations are widely used to study biological systems, where water molecules often play a critical role in protein-ligand interactions. In conventional MD preparation protocols, water molecules are typically added from a pre-equilibrated solvent box and removed using conservative steric cutoffs, an approach that can eliminate important interfacial waters that are often not recovered during equilibration due to kinetic barriers limiting exchange with bulk solvent. In this work, we present an automated and computationally efficient method for placing water molecules around biomolecular solutes using three-dimensional reference interaction site model (3D-RISM) solvent density distributions. By identifying regions of high solvent probability, the method generates physically meaningful initial hydration structures without requiring extended sampling or specialized techniques such as grand canonical Monte Carlo (MC) or hybrid MC/MD approaches, and will be released as an update to AmberTools 26, enabling seamless integration into standard MD preparation pipelines. We validate the approach on a diverse set of protein-ligand complexes with crystallographically resolved bridging waters, showing that 3D-RISM-based placement reproduces a large fraction of these experimentally observed waters, while subsequent minimization further improves agreement as crystallographic waters relax toward positions consistent with those predicted by our approach. Overall, this method enables more accurate and practical initialization of interfacial hydration, improving the reliability of MD simulations with modest computational cost relative to routine system preparation.
Zero-field (ZF) nuclear magnetic resonance (NMR) spectroscopy probes scalar J-couplings between nuclei while dispensing with large homogeneous magnetic fields, enabling low-cost and geometrically flexible detection, including through conductive enclosures. Despite these advantages, its broader use for chemical analysis has been limited by sensitivity and by the difficulty of predicting the dense spectral multiplets that arise at zero field. Here we demonstrate natural-abundance (1.1%) 13C ZF spectroscopy on off-the-shelf liquids using a compact commercial 87Rb magnetometer for the first time, without hyperpolarization or special sample preparation. Instrumental advances yield improved sensitivity, <250-mHz linewidths and >week-long stability, enabling isotopomer-resolved fingerprint spectra across a 13-molecule library, including the ability to discern rare (0.0121%) doubly 13C-labelled species. In parallel, we demonstrate vibrationally corrected density-functional theory (DFT) based prediction of ZF NMR spectra for chemically diverse molecules with few-hertz accuracy. Comparing experiment with these calculations renders residual deviations as chemically informative, reporting on hydrogen bonding, hydration and ion pairing at high ionic strength. Together, these results contribute towards DFT-assisted ZF NMR as a general platform for field-constraint-free molecular identification and for extracting transient solution-state structure from responsive J-coupling observables.
In this study, we present a quantum-statistical analysis of H$_2$ and LiH diatomic molecules within the Frost--Musulin potential framework. By combining the analytical bound-state approach to the radial Schr\"odinger problem with the near-equilibrium Pekeris representation, we obtain a validated rotation-vibration spectrum that reproduces a physically consistent ordering of energy levels. These bound states are subsequently combined with standard translational and rotational ideal gas contributions to construct the total partition function and the corresponding thermodynamic observables of the ground state. The resulting formulation captures the Gibbs free energy deviation function for both molecules with high quantitative accuracy and provides chemically plausible trends for heat capacity and enthalpy increase over a wide temperature range. At the same time, residual errors become increasingly pronounced in derivative-sensitive quantities, particularly at high temperatures; this indicates that the dominant limitations now stem not from the local bound-state spectrum itself, but from the neglect of inelastic rotational, continuity contributions and dynamics close to dissociation. Consequently, the present results define the potential model as a compact and analytically tractable representation of the bound region, recovering a significant portion of the observed thermochemistry whilst also delineating the regime where more comprehensive molecular statistical mechanics is required.
Review compares learning methods and flags transferability gaps that still limit everyday engineering use
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Recent advances in combustion science have led to the generation of large volumes of data from high-fidelity simulations, detailed chemical-kinetic calculations and engine-relevant measurements and create new opportunities for data-driven modelling across interacting physical and chemical scales. Among these approaches, artificial intelligence has emerged as a promising framework for constructing surrogate models that reduce computational costs, deliver substantial speed-up and support prediction in complex reacting systems. This review provides a state-of-the-art assessment of AI-powered surrogate modelling for multiscale combustion, spanning chemical kinetics, mechanism reduction, turbulent flames, combustors, engines, and emissions prediction. Supervised, unsupervised, and hybrid or physics-guided learning approaches are examined and compared in terms of predictive accuracy, physical consistency, computational efficiency, and generalizability across conditions and scales. The review further discusses key challenges, including limited transferability across fuels and operating regimes, extrapolation errors, inconsistency in datasets and benchmarks, and the difficulty of building robust and trustworthy models for practical combustion workflows. Future opportunities are identified in the development of more reliable, scalable, and physically grounded surrogate frameworks for next-generation combustion research.
Surface hopping (SH) methods are typically employed to simulate ultrafast nonadiabatic processes, but long timescales often remain beyond their reach. To address this, accelerated SH scheme mitigate this limitation by scaling the driving forces of such process, either nonadiabatic couplings (NACs) in case of internal conversion or spin-orbit couplings (SOCs) for intersystem crossing. However, obtaining the actual time constant requires extrapolation from several ensembles of trajectories with different scaling factors. This introduces a significant computational demand, often restricting the number of trajectories per ensemble and, therefore, reducing the statistical confidence in the resulting time constant. In this work, we investigate the accelerated scheme using silaethylene (CH$_2$SiH$_2$) as a case study, evaluating various population fitting methods and extrapolation techniques. We trained machine learning models for potential energy surfaces (PESs) and NACs, and extended our rotate-predict-rotate approach to fit SOCs. These models demonstrate high performance, yielding populations within the confidence interval of the reference MR-CISD/SA-CASSCF(2,2) data; however, the extrapolation itself is highly sensitive to the fitted time constants, leading to discrepancies in the final time constant. Finally, we showcase and discuss how ML models can enhance the reliability of an accelerated SH scheme.
In the theory of open quantum systems, spectral densities are key quantities for modeling the dynamics and spectroscopic properties of the system under investigation. In the case of light-harvesting complexes, they encode the frequency-dependent coupling of electronic excitations in pigment molecules to their environment, reflecting contributions from both intrinsic vibrational modes and the protein surrounding. In particular, the low-frequency components of the spectral densities are crucial for exciton transfer between pigment molecules. Apparently, slow internal modes of bacteriocholophyll molecules in the gas phase are less well represented by common force fields based on classical molecular dynamics (MD) simulations. Here, we demonstrate that Born-Oppenheimer molecular dynamics (BOMD) based on the numerically efficient density functional-based tight-binding approach can accurately recover these low-frequency features, whereas normal mode analysis captures them only partially. In contrasting approaches for determining spectral densities, the low-frequency region of the spectral densities obtained is only associated with protein fluctuations; the usage of BOMD, however, also captures the low-frequency contributions arising from slow intramolecular vibrations of the pigment molecules themselves. Notably, this behavior is consistently observed for both the flexible B800 and the more rigid B850 rings in light-harvesting 2 (LH2) complexes of purple bacteria, as well as in the Fenna-Matthews-Olson (FMO) complex of green sulfur bacteria. Interestingly, we also find that the spectral densities of the pigments in the B850 ring of LH2 are not influenced by the environment, i.e., the gaps between ground and first excited state are not changed significantly by the fluctuations of the protein environment.
We propose a simple and efficient pathway for the formation of precursors to core nucleobases in DNA and RNA using a suite of computational chemistry methods. Benzene, which is thermochemically stable in N2- or CO2-dominated atmospheres, could have formed via upper-atmospheric photochemistry or surface lightning and accumulated on the early Earth or Mars. However, nitrogen insertion into the benzene ring to form pyrimidine and purine is widely considered to be challenging. We propose that nitrogen incorporation occurred through HCN 1,4-cycloaddition to benzene's {\pi}-system, followed by a C2H2 fragmentation mechanism, as confirmed by quantum chemistry calculations. This pathway, potentially facilitated by photochemistry at the ocean surface or episodic impact events on local reservoirs, can lead to pyrimidine formation, which can further react with NH3 and HCN to produce purine. Extending this pathway to early Mars, our photochemical model simulates heterocyclic compound formation under cold, dry surface conditions that favor high benzene and HCN concentrations but lack liquid water. We thus propose that organics formed during dry phases may have later dissolved into surface waters during wet phases and become concentrated as ocean sediments. This result supports Mars Sample Return efforts focused on ancient aqueous environments likely to retain prebiotic signatures.
The Born-Oppenheimer surface and its derivatives emerge from composing the external-potential functional with the nuclear-to-Coulomb map, in
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Force fields are usually formulated directly in nuclear configuration space, whereas density functional theory is naturally formulated in terms of external potentials, densities, and variational duality. We show that exact force fields are variationally induced by DFT: the Born-Oppenheimer potential-energy surface is the pullback of the external-potential energy functional along the map from nuclear configurations to Coulomb potentials. In the Lieb formulation of density functional theory, the density is the first functional derivative of the energy with respect to the external potential, while the density-density response function is the second. Pulling these derivative objects back to nuclear configuration space yields the force and the nuclear Hessian, together with explicit terms induced by the nuclear-generated potential and the nuclear-nuclear repulsion. The resulting picture places force fields, density functional theory, and response theory within a single derivative hierarchy. The purpose of the present work is conceptual rather than algorithmic.
Action-detected two-dimensional electronic spectroscopy (A-2DES) could potentially be a versatile chemical tool with applicability across a range of photophysical observables such as photocurrent, photoionization, or fluorescence. However, a prominent absence of excited state energy/charge transfer dynamics signals in archetypal photosynthetic proteins has suggested severe limitations of A-2DES in probing large aggregates where sensitivity to excited state dynamics is proposed to go down as 1/N, where N is the aggregate size. We report measurements of energy transfer dynamics in a cyanobacterial protein through both conventional and fluorescence 2DES (F-2DES), where the dynamics reported by F-2DES is quite prominent and comparable to that measured by conventional 2DES. Analysis of our experiments combined with coarse-grained simulations of the spectra suggest that the 1/N limit argument, which assumes infinitely fast intra-exciton manifold equilibration, is modified in case of cyanobacterial proteins because of slow annihilation. Our results suggest that action detection may in fact be well-suited to probe exciton diffusion across weakly coupled systems.
Thermal conductivity of aligned polymer molecules can be exceptionally high along the alignment direction due to energy transport through strong covalent bonds. At the same time, it is highly sensitive to molecular conformation, varying by orders of magnitude as a result of gauche kinks. Here, we theoretically investigate phonon transport in kinked polymers by numerically evaluating thermal conductivity and interpreting the results in terms of phonon scattering from randomly distributed kinks. For strongly aligned polymers with restricted deviations from a linear backbone, we find that heat transport becomes superdiffusive at long lengths, with thermal conductivity scaling as $\kappa \propto L^{1/3}$. At shorter lengths, thermal conductivity exhibits non-monotonic behavior: it increases at very short scales due to ballistic transport of almost all phonons, then decreases at intermediate lengths due to the Anderson localization of most phonon modes. These results are consistent with experiments and molecular dynamics simulations, and they elucidate the microscopic mechanisms governing heat transport in polymers.
We present a theoretical and numerical study of the correlation between electrons and the fermionic $^{13}$C and $^{19}$F nuclei. We use the random-phase approximation (RPA) as a valuable tool in obtaining these correlation energies. A special connection between the RPA and second-order perturbation theory for the inter-fermionic interaction is outlined. Subsequently, Green's function based $GW$ self-energies are evaluated for the nuclear densities. The strong influence of self-interaction errors is outlined, and vertex corrections are shown to be strictly necessary to obtain reasonable results. The theoretical and technical requirements for a quantum mechanical treatment of $^{13}$C and $^{19}$F nuclei are also addressed in this work, thereby facilitating further research in this area.
Light-activated drugs are a promising way to treat localized diseases for which existing treatments have severe side effects. However, their development is complicated by the set of photophysical and biological properties that must be simultaneously optimized. Here we used computational techniques to find a set of promising candidates for the photoactive inhibition of the poly(ADP-ribose) polymerase 1 (PARP1) cancer target. Using our recently developed methods based on atomistic simulation and machine learning (ML), we screened a set of 5 million hypothetical photoactive ligands. Our workflow used protein-ligand docking to identify candidates with differential PARP1 binding under light and dark conditions; ML force fields and quantum chemistry calculations to predict p$K_\mathrm{a}$, absorption spectra, and thermal half-lives; graph-based surrogate models to screen additional compounds; excited-state nonadiabatic dynamics with ML force fields to estimate quantum yields; and free energy perturbation (FEP) to refine binding predictions. From these predictions, we prioritized a small set of synthetically feasible candidates expected to have red-shifted absorption spectra, thermal half-lives on the order of seconds to minutes, and isomer-dependent PARP1 binding under visible-light control. We synthesized 10 candidates and experimentally characterized their photobehavior and PARP1 inhibition constants. Among the validated compounds, \textbf{1} showed a 15-fold increase in inhibition of PARP1 upon green-light irradiation at 519 nm (208.8 $\pm$ 28.3 $\mu$M vs 14.4 $\pm$ 1.9 $\mu$M). These results validate the computation-guided screening strategy for identifying red-shifted PARP1 photoinhibitors, while also underscoring current limitations such as rapid thermal relaxation in aqueous media.
Machine-learning models of atomic-scale interactions achieve the accuracy of the quantum mechanical calculations on which they are trained, but at a dramatically lower computational cost. Their predictions can be made trustworthy by uncertainty quantification techniques that estimate the residual error relative to their reference. These errors, however, do not include uncertainty contributions from the approximations inherent in the electronic structure calculations, which are often the main source of discrepancy with empirical observations. We construct an ensemble of ML potentials trained on multiple electronic-structure references and calibrate it against experimental data on cohesive energies, atomization energies, lattice constants and bulk moduli of simple materials and molecules, similar to the uncertainty-aware functional distribution approach. The resulting ensemble of models, which we call PET-UAFD, can be used to simulate matter across a wide range of compositions and thermodynamic conditions. By comparison with experimental measurements of the density and structure of liquids, we demonstrate that, even outside the static properties on which it was calibrated, PET-UAFD enables predictions that are as accurate against experiments as the best available electronic-structure reference and that the spread in the ensemble can be used to assess the reliability of such predictions. We also introduce the PET-EXP protocol that uses shallow ensembles and statistical reweighting techniques to provide accurate estimates of uncertainty relative to experimental measurements at virtually no additional cost over a simulation based on a single conventional ML potential. Ultimately, this approach provides a practical and inexpensive approach to elevate machine-learning potentials from faithful interpolators of approximate theories to genuinely predictive tools anchored in experimental reality.
Machine-learning interatomic potentials (MLIPs) have enabled molecular dynamics at near ab initio accuracy, yet remain limited to energies and forces by construction, leaving electronic observables such as dipole moments and polarizabilities inaccessible. We introduce DenSNet, a density-first approach to machine-learned electronic structure that learns the Hohenberg--Kohn map from nuclear configurations to the ground-state electron density. Our approach employs an SE(3)-equivariant neural network to predict density coefficients of a flexible atom-centered Gaussian basis, combined with a $\Delta$-learning strategy that uses superposed atomic densities as a prior to accelerate training. A second equivariant network then maps the predicted density to the total energy, providing a unified framework for molecular dynamics and electronic structure. We validate DenSNet on ethanol, ethanethiol, and resorcinol, where infrared spectra from machine-learned trajectories show excellent agreement with experimental gas-phase measurements. To test scalability, we train on polythiophene oligomers with 1--6 monomers and extrapolate to chains of up to 12 monomers, generating stable long-time trajectories whose infrared spectra agree with reference density functional theory calculations. Here, we show that reinstating the electron density as the central learned quantity opens a practical route to transferable prediction of spectroscopic and electronic observables in large-scale molecular simulations.
Retrieving or generating two-dimensional molecular structures on the basis of vibrational spectra has been well demonstrated via deep learning models. However, deciphering three-dimensional molecular conformations is still challenging, primarily due to spectral ambiguities caused by conformational heterogeneity, which are difficult to resolve. To address this limitation, we propose Vib2Conf, a deep learning model directly discriminating 3D molecular conformations from vibrational spectra. We implement an attentional resampler to distill conformation-sensitive features from sparse spectral signals, and integrate Mixture-of-Experts (MoE) to partition the conformational space for precise geometric mapping. These modules enable Vib2Conf to achieve state-of-the-art top-1 recall exceeding 95% on traditional spectrum-structure benchmarks, including QM9S, VB-Mols, and QMe14S. More importantly, Vib2Conf can discriminate near-isomeric conformers with a top-1 recall of 82.06% on VB-Confs test set, where conformational isomers differ by a root-mean-square deviation (RMSD) of only ~1 {\AA}. In general, Vib2Conf is a promising method for fine-grained spectrum-to-conformation analysis.
The thermal cis-trans isomerization around the C$_{13}$=C$_{14}$ double bond of retinal is a prototypical high-barrier reaction whose mechanism hinges on subtle out-of-plane bending motions. We apply Artificial Intelligence for Molecular Mechanism Discovery (AIMMD) to N-retinylidene-lysine in vacuum, learning the committor from unbiased molecular dynamics trajectories generated by two-way shooting. Parametrizing the logit of the committor, rather than the committor itself, allows the neural network to resolve the reaction coordinate across the full transition region, not only at the isocommittor surface $p_B(\mathbf{x}) = 0.5$. Holdback input randomization identifies four proper dihedrals around the reactive bond as the informative coordinates, while the improper dihedrals at C$_{13}$ and C$_{14}$ prove unsuitable because reactant, transition, and product states share the same values. Symbolic regression then distills the network into compact analytical expressions and shows that a nonlinear coupling of all four dihedrals is required to reproduce the S-shaped, stepwise pathway seen in the transition path ensemble. This S-shape is absent from the minimum-free-energy path: it arises from the non-equilibrium dynamics of the short ($\sim 0.13$ ps) transition events combined with the mass asymmetry between heavy-atom and hydrogen-bearing dihedrals. An interpretable, machine-learned committor thus exposes dynamical features of the mechanism to which the free-energy surface is blind. The workflow requires no prior assumptions about the reaction coordinate and extends naturally to other isomerizations and to chemical reactions more broadly.
Sudden nuclear charge change is handled by overlaps along a lambda path, with SVD transport ensuring stability for bound and continuum state
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Electronic final states generated by sudden changes of the Hamiltonian are studied here, with emphasis on nuclear charge variation in $\beta$ decay. A $\lambda$-parametrized family $\hat H(\lambda)$ that continuously connects the initial and final Hamiltonians, so that the electronic response can be represented as a continuous deformation in Hilbert space, is introduced. Within the sudden approximation, transition amplitudes are written as overlaps between eigenstates of distinct Hamiltonians. To relate non-orthogonal one-electron basis sets in a stable way, the paper uses a practical transport scheme based on overlap metrics and truncated singular value decomposition (SVD). This mapping is interpreted as a discrete counterpart of continuous transport along the $\lambda$ path. The formalism is first developed for the one-electron case, where analytic structure and selection rules are made explicit, and then generalized to many-electron systems via nonorthogonal determinant overlap expressions. The resulting formulation gives transition probabilities in bound and continuum channels in a way that is both numerically stable and easy to interpret.
Time-frequency maps show B-mode decay and A-mode growth matching pre-dissociation and solvent recombination, indicating nonadiabatic mediat
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Vibrational wavepacket dynamics in the ground (X) and excited (B) electronic states of iodine under impulsive-pump/broadband-probe excitation are revisited. A method for accurate chirp correction, necessary to determine the zero time for each component of spectrally dispersed data and thereby separate coherent vibrational dynamics from coherent artifacts and population kinetics, is introduced. While from these processed time-domain data the absolute Raman cross-section in the ground electronic state can be calculated using steady-state absorption, we show that the same can be done using the pump-probe data itself, and further extend this method as a benchmark to calculate the same for the excited electronic state; these cross-sections report on vibronic couplings specific to these states. Further, since the Fourier transform of the processed data yields information on vibrational modes averaged over the dephasing time, a wavelet analysis is performed to yield a joint time-frequency distribution of the vibrational modes, demonstrating how the time evolution of their frequencies can be extracted. The vibrational modes of the ground and excited electronic states are shown to exhibit distinct dispersion characteristics. Since overlapping spectral features appear at different time windows, such an analysis can disentangle spectral congestion, even from a simple one-dimensional measurement. Most interestingly, a rapid time-dependent spectral shift and decay of the B state mode, followed by the appearance and growth of the A-state mode, directly correlates with the pre-dissociation, followed by solvent caging-induced recombination. Thus, the present work reveals transfer of vibrational coherence from one electronic state (B) to another (A), mediated via nonadiabatic coupling to the intermediate dissociative state (a), underscoring the importance of electronic coherence.
Drug-Filled Porous Implants (DFPIs) are an innovative solution for delivering drugs in a controlled and sustained manner to target sites. To optimize their performance across various physiological conditions, it is essential to understand how fluid flow and porous media properties influence the drug release process. In this work, we numerically investigate a wide range of flow conditions and their effects on drug release from DFPI. The DFPI is modeled as a homogeneous, saturated porous medium, with flow through the porous structure modeled using the Forchheimer-extended Darcy law. Drug diffusion within the DFPI and its transport through the surrounding channel are simulated using a diluted species transport approach. The results reveal the impact of flow conditions and porous media characteristics on the drug release profile of the implant and drug availability within the channel. The variations in drug release behavior are analyzed by modeling the release as an apparent first-order process with a time-dependent rate constant. Notably, the results highlight specific conditions under which the rate constant increases during the later stages of drug release from the DFPI, particularly at high Reynolds numbers, while also ensuring a prolonged operational time period of the implant. These findings suggest the potential for developing intelligent DFPI designs capable of delivering drugs in a manner more attuned to the specific needs of the application.
