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AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications

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Pith's one-line read AEGISS claims that combining single-orbital entropy screening with atomic-orbital projection reliably identifies compact, chemically meaningful active spaces for strongly correlated molecules.

desk verdict AEGISS is a sensible synthesis of AutoCAS entropy screening and AVAS-style AO projection that recovers the canonical active spaces on benzene and ferrocene, but the load-bearing entropy pre-filter has no convergence tests and the headline 'reliably identify' claim is undercut by the manual refinement required in the Ru case studies. read the letter →

arxiv 2508.10671 v3 pith:UUZRXBW5 submitted 2025-08-14 physics.chem-ph cond-mat.str-elphysics.comp-phquant-ph

Fabio Tarocco , Pi A. B. Haase , Fabijan Pavošević , Vijay Krishna , Leonardo Guidoni , Stefan Knecht , Martina Stella This is my paper · ORCID
classification physics.chem-phcond-mat.str-elphysics.comp-phquant-ph
keywords activespaceselectionsingle-orbitalentropyDMRGatomicvalenceAVASAutoCASCASSCF/NEVPT2Ru(II)photodynamictherapy
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper introduces AEGISS, a workflow that turns active-space selection from a heuristic expert guess into a two-stage, semi-automated procedure. First, an approximate DMRG calculation assigns each orbital a single-orbital entropy, and orbitals above a fraction of the maximum entropy are kept as a candidate pool. Second, a projection onto user-specified atomic-orbital labels on chosen atom clusters filters that pool down to the orbitals with the right chemical character. Applied to benzene, ferrocene, a TADF emitter, and two Ru(II) photodynamic-therapy complexes, the workflow recovers canonical or chemically documented active spaces that support SA-CASSCF, NEVPT2, and DMRG calculations. If the claim holds, AEGISS gives chemists and quantum-computing users a reproducible starting point that still allows expert refinement.

What carries the argument

The single-orbital entropy $S^{(1)}_p = -\sum_{\alpha=1}^4 \omega_{\alpha,p} \ln \omega_{\alpha,p}$, computed from the one-orbital reduced density matrix of an approximate DMRG/MPS wavefunction, is the correlation pre-filter; orbitals with $S^{(1)}_p > S^{(1)}_{\max}/10$ form the candidate pool. The second filter is the AVAS-style AO projection weight $w^D_p = \sum_{\eta\in D} \sum_\mu [S_D]_{\eta\mu} [C^A_E]_{\mu p}$, built from overlaps of a minimal-basis AO label set $D$ with the entropy-selected MOs. This weight selects the chemically relevant subset, and the union over all AO groups $D$, optionally refined, is the final active space.

What would settle it

Recompute the single-orbital entropies for ferrocene or Trans-Cl with DMRG bond dimensions $m=500$ and $m=2000$ and count the orbitals with $S^{(1)}_p > S^{(1)}_{\max}/10$; if the candidate pool or the final CAS($n_e,n_o$) changes, the entropy pre-filter is not converged. Also enlarge the Fermi-level window $N_A$ until the high-entropy orbitals are strictly interior; if new high-entropy orbitals appear outside $N_A$, the pre-filter missed relevant physics.

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Extended reading notes

Core claim

The central claim is that combining orbital-entropy screening with atomic-orbital projection is sufficient to identify compact, chemically intuitive active spaces across correlation regimes. The entropy step, computed from a low-bond-dimension DMRG/MPS wavefunction, gathers the strongly correlated frontier orbitals; the projection step then keeps, per atomic cluster and per AO label, only those orbitals whose projection weight exceeds a threshold or ranks highest. Unlike a single global AVAS projector, the per-region labels let metal 4d and ligand π channels be selected independently, which the authors argue is why the Ru(II) complexes avoid the metal–ligand mixing artifacts they attribute t

Load-bearing premise

The load-bearing premise is that orbitals outside the user-chosen Fermi-level window $N_A$ or below the entropy cutoff $\tau_E = S^{(1)}_{\max}/10$ are irrelevant to the final active space; the paper advises verifying that highly entropic orbitals lie inside the window but reports no such verification or bond-dimension convergence test.

Editorial extensions

If this is right

  • If AEGISS works as claimed, active-space selection no longer requires a single global projector: separate AO groups for metal and ligand can be selected independently, which directly addresses mixed metal–ligand states in Ru(II) complexes.
  • The benzene and ferrocene results imply the workflow recovers the same orbital manifold as AVAS/CASSCF when starting from canonical RHF orbitals, so users can trust the pipeline for benchmark-quality spaces without pre-rotating molecular orbitals.
  • The per-cluster labeling plus entropy prescreen gives a reproducible, semi-automated protocol that lowers the trial-and-error burden in multireference calculations on photoactive molecules.
  • For quantum computing, the compact spaces generated, e.g. CAS(14,13), are small enough to map onto qubits, making AEGISS a practical preprocessing step for near-term and fault-tolerant molecular simulations.
  • Because the AO-projection cost is negligible compared with the DMRG step, refining atom labels is cheap and can be repeated without rerunning the correlated calculation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the entropy pre-filter is stable, AEGISS could be pushed toward full automation by replacing the user-supplied AO labels with unsupervised localization or clustering of high-entropy orbitals; the paper only suggests machine-learning-assisted prescreening as future work.
  • The per-region AO selection could serve as a natural interface for embedding and quantum algorithms: each cluster defines a separate correlation channel, so effective Hamiltonians for different fragments could be prepared independently and then coupled; the paper notes the quantum-computing motivation but does not develop this.
  • The DOBNA results imply the initial mean-field guess is a hidden design choice: RHF, MP2 natural orbitals, and MP2 frozen natural orbitals produce different final spaces, so fair AEGISS usage on systems with Rydberg-like virtuals should compare at least two guesses.
  • A direct boundary test would be to apply AEGISS to a molecule where the chemically essential orbital is known to lie just outside the Fermi window or just below the entropy cutoff; if the final space misses it, the selective power comes from the AO projection and the entropy step is redundant.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

1 major / 7 minor

Summary. The manuscript introduces AEGISS, a semi-automated active-space selection workflow combining (i) a DMRG-based single-orbital entropy prescreen, inspired by AutoCAS, and (ii) an AVAS-like atomic-orbital projection step, with optional per-region atom labels. The method is applied to benzene, ferrocene, the TADF emitter DOBNA, and two Ru(II) PDT-relevant complexes, and the resulting active spaces are validated with CASCI/CASSCF, NEVPT2, DMRG-CI, and comparisons to AVAS, TD-DFT, literature multireference data, and experiment. The authors report recovery of the canonical CAS(6,6) for benzene and CAS(10,7) for ferrocene, and compact spaces for DOBNA, Trans-Cl, and TLD-1433, and frame the method as a scalable, reproducible alternative to heuristic active-space selection for both classical and quantum-computing workflows.

Significance. If the reported reliability is borne out, AEGISS is a useful contribution: it packages two established ideas, entropy-based preselection and AO-projection selection, into one open-source workflow, and its per-region AO labels address a real limitation of the single-projector AVAS construction. The ferrocene NEVPT2 excitation energies matching the AVAS reference are a concrete, checkable strength, and the public repository with inputs/outputs is a reproducibility-positive feature. However, the evidence presented does not yet establish the central reliability claim for challenging systems: the two largest test cases require manual repair of the automatically generated space, and the entropy prescreen that gates all subsequent selection is never subjected to a convergence or sensitivity test. The paper is best viewed as a promising method paper whose headline claim needs either additional validation or a more carefully calibrated scope.

major comments (1)
  1. [Section 3.1, Step 3 (Eq. (4)); Tables 1, 3, 5, 7, 9] The DOBNA results expose a large sensitivity to the entropy threshold: Spaces IV and VI differ only in the initial-guess orbital set and screening procedure, yet their NEVPT2 singlet-triplet gaps differ by 0.29 eV (0.731 vs 0.443 eV), and neither is close to the experimental value of ~0.15 eV (all methods overestimate). The paper does not provide a principled criterion for choosing among the six generated spaces, or a diagnostic for why space VI should be preferred over space IV. This is not necessarily a flaw of a semi-automated workflow, but it weakens the 'reliably identifies the essential physics' claim for this application class and should be addressed explicitly by the authors.
minor comments (7)
  1. [Table 1 and Section 4.1] Benzene uses tau_E = 0.2 (20% of S_max), whereas the workflow in Section 3 and Eq. (4) specifies tau_E = S_max/10, and all other case studies use 0.1. Harmonize or justify the deviation.
  2. [Table 3 vs. Section E.1] The ferrocene setup is described as cc-pVTZ-DK in Table 3 but cc-pVDZ-DK in the Supporting Information. Correct the inconsistency.
  3. [Eq. (3)] The one-orbital RDM expression is hard to parse and the notation is nonstandard: the object is diagonal in the local occupation basis, and Eq. (15) in the SI gives the actual eigenvalues. Please rewrite Eq. (3) as a partial trace over all orbitals except p, or state explicitly that only the diagonal occupation probabilities are needed.
  4. [Table 5 and Section E.2] For MP2-NOs with tau_E=0.1, the text says no orbitals are removed because all relative SOEs remain above the threshold. This should be stated clearly in Table 5 or its caption, since it means the entropy screen is ineffective for that candidate space.
  5. [Figure 1 caption] The caption says 'C6H6 molecule in cc-pVTZ basis' and a DMRG(30,30) with m=200, while Section 4.1 reports cc-pVDZ, NA=30, m=500. Align the figure caption with the actual calculation or label it as a separate illustrative calculation.
  6. [SI Table 11] Space I for DOBNA did not converge within the maximum number of SCF cycles. The main text should mention this explicitly, since Tables 5-6 otherwise imply all six spaces are equally validated.
  7. [Throughout] There are repeated typos ('T able', 'actie', 'Pyhs.' in Ref. [10], 'Moller' formatting) and inconsistent basis-set names. A careful copyedit is needed.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity: external benchmarks carry the validation; minor self-citation and manual refinement caveats.

full rationale

The workflow's derivation chain is self-contained: Step 3 (Eq. 4) ranks orbitals by single-orbital entropy from an approximate DMRG wavefunction; Step 5 (Eqs. 5-7) projects the entropy-selected MOs onto user-specified AO labels. The final active space is the union of orbitals passing both screens. No step defines the target active space in terms of the benchmark excitation energies or literature CAS spaces. For benzene and ferrocene, the recovered CAS(6,6) and CAS(10,7) spaces are validated against AVAS and literature, and the benzene CASSCF energies converge to the same value for both selections (Table 2), an external check. For DOBNA and the Ru complexes, the paper explores multiple candidate spaces and compares against TDDFT/literature; the closest-to-experiment space (DOBNA VI) is not singled out a priori, but the comparison is across a scanned set rather than a fitted parameter. The paper explicitly acknowledges manual refinement in Trans-Cl Space II ('replacing HOMO−13 and HOMO−12 with HOMO−1 and HOMO') and TLD-1433 Space II ('the Rydberg orbital can be removed and the HOMO restored'); these are transparent semi-automated steps based on orbital character and entropy, not on the target energies, so they do not make the validation circular. However, they qualify the 'reliably identify' claim for the most challenging systems. Self-citations to the companion ADAPT-VMPE study [44] and QICAS [84] are minor and not load-bearing: the selection results are benchmarked externally, not by those citations. The absence of DMRG bond-dimension/window convergence tests is a correctness/robustness gap, not a circularity. Overall: no significant circularity; score 2 reflects the minor self-citation and the manual-refinement caveat.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The workflow contributes a protocol, not new physics: it imports the S_max/10 entropy threshold from AutoCAS and the minimal-basis AO projection from AVAS, and adds user-tunable thresholds, top-k orbital counts, large-CAS window sizes, and DMRG bond dimensions. The DOBNA study multiplies these into a six-space scan, and the Ru complexes undergo manual refinement, so the effective number of post-hoc choices exceeds the advertised settings. The central untested assumption is that cheap-DMRG entropies over a user-chosen window form a faithful pre-filter for the AO projection.

free parameters (6)
  • Entropy threshold tau_E = S_max/10 (default); 0.2 for benzene; 0.9 for DOBNA spaces II/IV/VI
    Controls which orbitals survive step 3. Inherited from AutoCAS (Ref. 41) but varied per system; DOBNA uses two thresholds to generate six candidate spaces, and the space that best matches experiment is emphasized.
  • AO projection weight threshold epsilon_D = 0.5
    Second screening cutoff; user-chosen. Replaced by top-k counts n_D for the Ru complexes.
  • Top-k orbital count n_D = [1] or [2] per Ru 4d label and 4 or 6 per ligand pi label (Trans-Cl); (5,5)/(7,7) etc. (TLD-1433)
    User-specified number of orbitals per AO label; directly sets active space size for the Ru cases.
  • Large-CAS window N_A = 30 (benzene); 40 (ferrocene, DOBNA); 90 (Trans-Cl); 100 (TLD-1433)
    Defines the candidate pool for entropy screening; chosen per system around the Fermi level with no reported sensitivity test.
  • DMRG bond dimension m = 100 to 500 (or fixed 500); 1000 for TLD-1433
    Computational parameter that determines entropy accuracy; no convergence study relative to final selection is reported.
  • Initial guess for DOBNA = Three initial guesses (RHF, MP2-NO, MP2-fNO) x two thresholds (0.1, 0.9)
    The sixfold DOBNA scan functions as a post-hoc search; space VI (MP2-fNO, tight threshold) is highlighted because it agrees best with experiment.
assumptions (5)
  • domain assumption The standard electronic structure machinery (HF, CASSCF, NEVPT2, DMRG, TD-DFT, PCM) correctly describes the five target molecules.
    Used throughout Section 4 to validate selected spaces; e.g., SA-CASSCF multireference energies are taken as ground truth against reference values.
  • domain assumption Single-orbital entropies from an approximate low-bond-dimension DMRG (m=500-1000) faithfully identify strongly correlated orbitals within a user-chosen window.
    Section 3 step 3 and Section 3.1; no bond-dimension convergence check is reported.
  • domain assumption The user's atom labeling and AO labels correctly encode where the relevant chemistry occurs.
    Step 4 and SI Section B; the Ru benchmarks depend on the choice of Ru 4d and ligand 2pz labels; if labeling is skipped, AEGISS reduces to an AVAS-like limit.
  • standard math Minimal-basis AO overlap is a faithful measure of the chemical character of a molecular orbital.
    Step 5; borrowed unchanged from AVAS (Ref. 40).
  • domain assumption The S_max/10 entropy cutoff rule from AutoCAS is transferable to the systems studied here.
    Step 3b; taken from Ref. 41, and modified (0.2, 0.9) without justification of transferability.

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Cite this review

Pith. "Pith review of AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications." pith.science (2026). https://pith.science/paper/UUZRXBW5

@misc{pith2026250810671,
  author       = {Pith},
  title        = {Pith review of: AEGISS -- Atomic orbital and Entropy-based Guided Inference for Space Selection -- A novel semi-automated active space selection workflow for quantum chemistry and quantum computing applications},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/UUZRXBW5}},
  note         = {Machine review of arXiv:2508.10671}
}
read the original abstract

The selection of a balanced active space is a critical step in multi-reference quantum chemistry calculations, particularly for systems with strong electron correlation. Likewise, active space selection is a key to unlock the potential of contemporary quantum computing in quantum chemistry. Albeit recent progress, there remains a lack of a unified, robust, and fully automated framework for active space selection that performs reliably across a wide range of molecular systems. In this work, we present a novel approach inspired by both the AVAS (Atomic Valence Active Space) and AutoCAS methods. Our method unifies orbital entropy analysis with atomic orbital projections to guide the construction of chemically and physically meaningful active spaces. This integrated scheme enables a more consistent and flexible selection of active orbitals while retaining automation and scalability. We validate our approach on a set of molecular systems relevant to photodynamic therapy, in particular a set of Ru(II)-complexes, selected to span increasing levels of electron correlation and structural complexity. These molecules serve as challenging test cases due to the presence of strong static correlation and the need for highly accurate electronic structure descriptions. Our results demonstrate that the method can reliably identify compact, chemically intuitive active spaces that capture the essential physics, making it suitable for both classical and quantum computational frameworks. Furthermore, we have developed this approach in a package that is intuitive to use for users and can be interfaced with both standard quantum chemistry and quantum computing applications, making it accessible to a broad research community.

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Reference graph

Works this paper leans on

138 extracted references · 41 canonical work pages · cited by 1 Pith paper

  1. [1]

    Inhomogeneous Electron Gas

    P. Hohenberg and W. Kohn. “Inhomogeneous Electron Gas”. In:Phys. Rev.136.3B (1964), B864– B871

    1964

  2. [2]

    Self-Consistent Equations Including Exchange and Correlation Effects

    W. Kohn and L. J. Sham. “Self-Consistent Equations Including Exchange and Correlation Effects”. In:Phys. Rev.140.4A (1965), A1133–A1138

    1965

  3. [3]

    A review on the use of DFT for the prediction of the properties of nanomaterials

    P. Makkar and N. N. Ghosh. “A review on the use of DFT for the prediction of the properties of nanomaterials”. In:RSC Adv.11 (45 2021), pp. 27897–27924.doi:10.1039/D1RA04876G.url: http://dx.doi.org/10.1039/D1RA04876G

    work page doi:10.1039/d1ra04876g.url: 2021

  4. [4]

    Reproducibility in density functional theory calculations of solids

    K. Lejaeghere et al. “Reproducibility in density functional theory calculations of solids”. In:Science 351.6280 (2016), aad3000.doi:10.1126/science.aad3000.url:https://www.science.org/ doi/abs/10.1126/science.aad3000

    work page doi:10.1126/science.aad3000.url:https://www.science.org/ 2016

  5. [5]

    PerspectiveonDensityFunctionalTheory

    K.Burke.“PerspectiveonDensityFunctionalTheory”.In:J. Chem. Phys.136.15(2012),p.150901

    2012

  6. [6]

    Multireference Approaches for Excited States of Molecules

    H. Lischka et al. “Multireference Approaches for Excited States of Molecules”. In:Chem. Rev. 118.15 (2018), pp. 7293–7361.doi:10.1021/acs.chemrev.8b00244.url:https://doi.org/10. 1021/acs.chemrev.8b00244

    work page doi:10.1021/acs.chemrev.8b00244.url:https://doi.org/10 2018

  7. [7]

    A complete active space SCF method (CASSCF) using a density matrix formulated super-CI approach

    B. O. Roos, P. R. Taylor, and P. E. Sigbahn. “A complete active space SCF method (CASSCF) using a density matrix formulated super-CI approach”. In:Chem. Phys.48.2 (1980), pp. 157– 173.issn: 0301-0104.doi:https://doi.org/10.1016/0301- 0104(80)80045- 0.url:https: //www.sciencedirect.com/science/article/pii/0301010480800450. 19

    arXiv 1980

  8. [8]

    Second-order perturbation theory with a CASSCF reference function

    K. Andersson et al. “Second-order perturbation theory with a CASSCF reference function”. In: J. Phys. Chem.94.14 (1990), pp. 5483–5488.doi:10.1021/j100377a012. eprint:https://doi. org/10.1021/j100377a012.url:https://doi.org/10.1021/j100377a012

    doi:10.1021/j100377a012 1990

Show all 138 references
  1. [9]

    Second-order perturbation theory with a com- plete active space self-consistent field reference function

    K. Andersson, P.-Å. Malmqvist, and B. O. Roos. “Second-order perturbation theory with a com- plete active space self-consistent field reference function”. In:J. Phys. Chem.96.2 (Jan. 1992), pp. 1218–1226.issn: 0021-9606.doi:10.1063/1.462209. eprint:https://pubs.aip.org/aip/ jc...

    1992 doi

  2. [10]

    Introduction of n-electron valence states for multireference perturbation theory

    C. Angeli et al. “Introduction of n-electron valence states for multireference perturbation theory”. In:J. Chem. Pyhs.114.23 (June 2001), pp. 10252–10264.issn: 0021-9606.doi:10 . 1063 / 1 . 1361246. eprint:https://pubs.aip.org/aip/jcp/article- pdf/114/23/10252/19105543/ 10252\...

    2001 doi

  3. [11]

    N-electron valence state perturbation theory: a fast implementation of the strongly contracted variant

    C. Angeli, R. Cimiraglia, and J.-P. Malrieu. “N-electron valence state perturbation theory: a fast implementation of the strongly contracted variant”. In:Chem. Phys. Lett.350.3 (2001), pp. 297– 305.issn: 0009-2614.doi:https://doi.org/10.1016/S0009-2614(01)01303-3.url:https: //...

    2001 doi

  4. [12]

    n-electron valence state perturbation theory: A spinless formulation and an efficient implementation of the strongly contracted and of the partially contracted variants

    C. Angeli, R. Cimiraglia, and J.-P. Malrieu. “n-electron valence state perturbation theory: A spinless formulation and an efficient implementation of the strongly contracted and of the partially contracted variants”. In:J. Chem. Phys.117.20 (Nov. 2002), pp. 9138–9153.issn: 002...

    2002 doi

  5. [13]

    Multiconfiguration Self-Consistent Field and Multireference Configuration Interaction Methods and Applications

    P. G. Szalay et al. “Multiconfiguration Self-Consistent Field and Multireference Configuration Interaction Methods and Applications”. In:Chem. Rev.112 (2012), pp. 108–181.doi:10.1021/ cr200137a

    2012

  6. [14]

    AnIntroductiontoCoupledClusterTheoryforComputational Chemists

    T.D.CrawfordandH.F.Schaefer.“AnIntroductiontoCoupledClusterTheoryforComputational Chemists”. In:Rev. Comput. Chem.14 (2000), pp. 33–136

    2000

  7. [15]

    Coupled-Cluster Theory in Quantum Chemistry

    R. J. Bartlett and M. Musiał. “Coupled-Cluster Theory in Quantum Chemistry”. In:Rev. Mod. Phys.79.1 (2007), p. 291

    2007

  8. [16]

    Multireference Nature of Chemistry: The Coupled-Cluster View

    D. I. Lyakh, M. Musiał, V. F. Lotrich, and R. J. Bartlett. “Multireference Nature of Chemistry: The Coupled-Cluster View”. In:Chem. Rev.112.1 (2012), pp. 182–243

    2012

  9. [17]

    Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations

    K. D. Vogiatzis et al. “Pushing configuration-interaction to the limit: Towards massively parallel MCSCF calculations”. In:J. Chem. Phys.147.18 (2017)

    2017

  10. [18]

    New approaches for ab initio calculations of molecules with strong electron correlation

    S. Knecht et al. “New approaches for ab initio calculations of molecules with strong electron correlation”. In:Chimia70 (2016), pp. 244–251.doi:10.2533/chimia.2016.244

    2016 doi

  11. [19]

    The density matrix renormalization group in chemistry and molecular physics: Recent developments and new challenges

    A. Baiardi and M. Reiher. “The density matrix renormalization group in chemistry and molecular physics: Recent developments and new challenges”. In:J. Chem. Phys.152 (2020), p. 040903.doi: 10.1063/1.5129672

    2020 doi

  12. [20]

    Fermion Monte Carlo without fixed nodes: A game of life, death, and annihilation in Slater determinant space

    G. H. Booth, A. J. W. Thom, and A. Alavi. “Fermion Monte Carlo without fixed nodes: A game of life, death, and annihilation in Slater determinant space”. In:J. Chem. Phys.131 (2009), pp. 054106–11.doi:10.1063/1.3193710

    2009 doi

  13. [21]

    Fast semistochastic heat-bath configuration interaction

    J. Li et al. “Fast semistochastic heat-bath configuration interaction”. In:J. Chem. Phys.149.21 (2018), p. 214110.doi:10.1063/1.5055390

    2018 doi

  14. [22]

    A deterministic alternative to the full configuration interaction quantum Monte Carlo method

    N. M. Tubman et al. “A deterministic alternative to the full configuration interaction quantum Monte Carlo method”. In:J. Chem. Phys.145 (2016), p. 044112.doi:10.1063/1.4955109

    2016 doi

  15. [23]

    Comparison of Many-Particle Representations for Selected-CI I: A Tree Based Approach

    V. G. Chilkuri and F. Neese. “Comparison of Many-Particle Representations for Selected-CI I: A Tree Based Approach”. In:J. Comp. Chem.42 (2021), pp. 982–1005.doi:10.1002/jcc.26510

    2021 doi

  16. [24]

    iCI: Iterative CI toward full CI

    W. Liu and M. R. Hoffmann. “iCI: Iterative CI toward full CI”. In:Journal of Chemical Theory and Computation12.3 (2016), pp. 1169–1178.doi:10.1021/acs.jctc.5b01099

    2016 doi

  17. [25]

    Iterative Configuration Interaction with Selection

    N. Zhang, W. Liu, and M. R. Hoffmann. “Iterative Configuration Interaction with Selection”. In: J. Chem. Theory Comput.16.4 (2020), pp. 2296–2316.doi:10.1021/acs.jctc.9b01200

    2020 doi

  18. [26]

    Quantum Chemistry in the Age of Quantum Computing

    Y. Cao et al. “Quantum Chemistry in the Age of Quantum Computing”. In:Chem. Rev.119.19 (2019), pp. 10856–10915. 20

    2019

  19. [27]

    Quantum Algorithms for Quantum Chem- istry and Quantum Materials Science

    B. Bauer, S. Bravyi, M. Motta, and G. Kin-Lic Chan. “Quantum Algorithms for Quantum Chem- istry and Quantum Materials Science”. In:Chem. Rev.120.22 (2020), pp. 12685–12717

    2020

  20. [28]

    The Variational Quantum Eigensolver: A Review of Methods and Best Practices

    J. Tilly et al. “The Variational Quantum Eigensolver: A Review of Methods and Best Practices”. In:Phys. Rep.986 (2022), pp. 1–128

    2022

  21. [29]

    Quantum Computing for Photosensitizer Design in Photodynamic Therapy

    H. Zehr et al. “Quantum Computing for Photosensitizer Design in Photodynamic Therapy”. In: Annu. Rev. Biomed. Data Sci.8.Volume 8, 2025 (2025), pp. 509–536.issn: 2574-3414.doi: https : / / doi . org / 10 . 1146 / annurev - biodatasci - 103123 - 095644.url:https : / / www . ann...

    2025 doi

  22. [30]

    Prospects of quantum computing for molecular sciences

    H. Liu et al. “Prospects of quantum computing for molecular sciences”. In:Mater. Theory6.1 (2022), p. 11.issn: 2509-8012.doi:10.1186/s41313-021-00039-z.url:https://doi.org/10. 1186/s41313-021-00039-z

    2022 doi

  23. [31]

    Quantum Chemistry in the Age of Quantum Computing

    Y. Cao et al. “Quantum Chemistry in the Age of Quantum Computing”. In:Chem. Rev.119.19 (2019). PMID: 31469277, pp. 10856–10915.doi:10.1021/acs.chemrev.8b00803. eprint:https: //doi.org/10.1021/acs.chemrev.8b00803.url:https://doi.org/10.1021/acs.chemrev. 8b00803

    2019 doi

  24. [32]

    Quantum computing and chemistry

    J. D. Weidman et al. “Quantum computing and chemistry”. In:Cell Reports Physical Science5 (2024), p. 102105.doi:10.1016/j.xcrp.2024.102105.url:https://doi.org/10.1016/j. xcrp.2024.102105

    2024

  25. [33]

    Alexeev et al.A Perspective on Quantum Computing Applications in Quantum Chemistry using 25–100 Logical Qubits

    Y. Alexeev et al.A Perspective on Quantum Computing Applications in Quantum Chemistry using 25–100 Logical Qubits. arXiv: 2506.19337. 2025.url:https://arxiv.org/abs/2506.19337

    2025

  26. [34]

    Schleich et al.Quantum Computing for Quantum Chemistry

    P. Schleich et al.Quantum Computing for Quantum Chemistry. Washington, DC, USA: American Chemical Society, 2025.doi:10.1021/acsinfocus.7e9012. eprint:https://pubs.acs.org/ doi / pdf / 10 . 1021 / acsinfocus . 7e9012.url:https : / / pubs . acs . org / doi / abs / 10 . 1021 / ac...

    2025 doi

  27. [35]

    Über das Paulische Äquivalenzverbot

    P. Jordan and E. Wigner. “Über das Paulische Äquivalenzverbot”. In:Zeitschrift für Physik47.9-10 (1928), pp. 631–651

    1928

  28. [36]

    Fermionic Quantum Computation

    S. B. Bravyi and A. Y. Kitaev. “Fermionic Quantum Computation”. In:Ann. Phys.298.1 (2002), pp. 210–226.issn: 0003-4916.doi:https://doi.org/10.1006/aphy.2002.6254.url:https: //www.sciencedirect.com/science/article/pii/S0003491602962548

    2002

  29. [37]

    S.Bravyi,J.M.Gambetta,A.Mezzacapo,andK.Temme.Tapering off qubits to simulate fermionic Hamiltonians. 2017. arXiv:1701.08213 [quant-ph].url:https://arxiv.org/abs/1701.08213

    2017 arXiv

  30. [38]

    Bonsai Algorithm: Grow Your Own Fermion-to-Qubit Mappings

    A. Miller et al. “Bonsai Algorithm: Grow Your Own Fermion-to-Qubit Mappings”. In:PRX Quan- tum4.3 (2023).doi:10.1103/prxquantum.4.030314

    2023 doi

  31. [39]

    Miller, A

    A. Miller, A. Glos, and Z. Zimborás.Treespilation: Architecture- and State-Optimised Fermion-to- Qubit Mappings. 2024.doi:10.48550/ARXIV.2403.03992.url:https://arxiv.org/abs/2403. 03992

    2024 doi arXiv

  32. [40]

    Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals

    E. R. Sayfutyarova, Q. Sun, G. K.-L. Chan, and G. Knizia. “Automated Construction of Molecular Active Spaces from Atomic Valence Orbitals”. In:J. Chem. Theory Comput.13.9 (Aug. 2017), pp. 4063–4078.issn: 1549-9626.doi:10.1021/acs.jctc.7b00128.url:http://dx.doi.org/ 10.1021/acs...

    2017 doi

  33. [41]

    Automated Selection of Active Orbital Spaces

    C. J. Stein and M. Reiher. “Automated Selection of Active Orbital Spaces”. In:J. Chem. Theory Comput.12.4 (Apr. 2016), pp. 1760–1771.issn: 1549-9618.doi:10.1021/acs.jctc.6b00156. url:https://doi.org/10.1021/acs.jctc.6b00156

    2016 doi

  34. [42]

    Automated Identification of Relevant Frontier Orbitals for Chemical Compounds and Processes

    C. J. Stein and M. Reiher. “Automated Identification of Relevant Frontier Orbitals for Chemical Compounds and Processes”. en. In:Chimia (Aarau)71.4 (Apr. 2017), pp. 170–176

    2017

  35. [43]

    autoCAS: A Program for Fully Automated Multiconfigurational Cal- culations

    C. J. Stein and M. Reiher. “autoCAS: A Program for Fully Automated Multiconfigurational Cal- culations”. In:J. Comp. Chem.40.25 (2019), pp. 2216–2226.doi:https://doi.org/10.1002/ jcc.25869. eprint:https://onlinelibrary.wiley.com/doi/pdf/10.1002/jcc.25869.url: https://onlinelib...

    2019 doi

  36. [44]

    Chakraborty et al.Scalable quantum circuit generation for iterative ground state approximation using Majorana Propagation

    R. Chakraborty et al.Scalable quantum circuit generation for iterative ground state approximation using Majorana Propagation. 2026. arXiv:2603.23444 [quant-ph].url:https://arxiv.org/ abs/2603.23444. 21

    2026

  37. [45]

    MULTICONFIGURATIONAL QUANTUM CHEMISTRY FOR GROUND AND EXCITED STATES

    B. O. Roos. “MULTICONFIGURATIONAL QUANTUM CHEMISTRY FOR GROUND AND EXCITED STATES”. In:Radiation Induced Molecular Phenomena in Nucleic Acids. Ed. by M. K. Shukla and J. Leszczynski. Springer, 2008, pp. 125–156

    2008

  38. [46]

    Hu et al

    H. Hu et al. “Small Tensor Product Distributed Active Space (STP-DAS) Framework for Rela- tivistic and Nonrelativistic Multiconfiguration Calculations: Scaling from 109 on a Laptop to 1012 Determinants on a Supercomputer”. In:Chem. Phys. Rev.5 (2024), p. 041404

    2024

  39. [47]

    DistributedImplementationofFullConfiguration Interaction for One Trillion Determinants

    H.Gao,S.Imamura,A.Kasagi,andE.Yoshida.“DistributedImplementationofFullConfiguration Interaction for One Trillion Determinants”. In:J. Chem. Theory Comput.20 (2024), pp. 1185– 1192.doi:10.1021/acs.jctc.3c01190

    2024 doi

  40. [48]

    Shayit et al.Breaking the Quadrillion Determinant Barrier in Numerically Exact Configuration Interaction

    A. Shayit et al.Breaking the Quadrillion Determinant Barrier in Numerically Exact Configuration Interaction. arXiv: 2505.20375. 2025.url:https://arxiv.org/abs/2505.20375

    2025

  41. [49]

    Density matrix formulation for quantum renormalization groups

    S. R. White. “Density matrix formulation for quantum renormalization groups”. In:Phys. Rev. Lett.69 (19 1992), pp. 2863–2866.doi:10.1103/PhysRevLett.69.2863.url:https://link. aps.org/doi/10.1103/PhysRevLett.69.2863

    1992 doi

  42. [50]

    Density-matrix algorithms for quantum renormalization groups

    S. R. White. “Density-matrix algorithms for quantum renormalization groups”. In:Phys. Rev. B 48 (14 1993), pp. 10345–10356.doi:10.1103/PhysRevB.48.10345.url:https://link.aps. org/doi/10.1103/PhysRevB.48.10345

    1993 doi

  43. [51]

    The density-matrix renormalization group

    U. Schollwöck. “The density-matrix renormalization group”. In:Rev. Mod. Phys.77 (1 2005), pp. 259–315.doi:10.1103/RevModPhys.77.259.url:https://link.aps.org/doi/10.1103/ RevModPhys.77.259

    2005 doi

  44. [52]

    The density-matrix renormalization group in the age of matrix product states

    U. Schollwöck. “The density-matrix renormalization group in the age of matrix product states”. In:Annals of Physics326.1 (2011). January 2011 Special Issue, pp. 96–192.issn: 0003-4916.doi: https://doi.org/10.1016/j.aop.2010.09.012.url:https://www.sciencedirect.com/ science/art...

    2011 doi

  45. [53]

    Determining Factors for the Accuracy of DMRG in Chemistry

    S. Keller and M. Reiher. “Determining Factors for the Accuracy of DMRG in Chemistry”. In: Chimia68 (2014), pp. 200–203

    2014

  46. [54]

    Recent developments in the PySCF program package

    Q. Sun et al. “Recent developments in the PySCF program package”. In:J. Phys. Chem.153.2 (July 2020), p. 024109.issn: 0021-9606.doi:10.1063/5.0006074. eprint:https://pubs.aip. org/aip/jcp/article- pdf/doi/10.1063/5.0006074/16722275/024109\_1\_online.pdf. url:https://doi.org/10...

    2020 doi

  47. [55]

    PySCF: the Python-based simulations of chemistry framework

    Q. Sun et al. “PySCF: the Python-based simulations of chemistry framework”. In:WIREs Comput. Mol. Sci.8.1 (2018), e1340

    2018

  48. [56]

    Recent developments in the PySCF program package

    Q. Sun et al. “Recent developments in the PySCF program package”. In:J. Chem. Phys.153.2 (2020)

    2020

  49. [57]

    OpenMolcas: From Source Code to Insight

    I. Fdez. Galván et al. “OpenMolcas: From Source Code to Insight”. In:J. Chem. Theory Comput. 15 (2019), pp. 5925–5964

    2019

  50. [58]

    The OpenMolcas Web: A Community-Driven Approach to Advancing Com- putational Chemistry

    G. Li Manni et al. “The OpenMolcas Web: A Community-Driven Approach to Advancing Com- putational Chemistry”. In:J. Chem. Theory Comput.19.20 (2023), pp. 6933–6991.doi:10.1021/ acs . jctc . 3c00182. eprint:https : / / doi . org / 10 . 1021 / acs . jctc . 3c00182.url:https : //d...

    2023 doi

  51. [59]

    Highly correlated calculations with a polynomial cost al- gorithm: A study of the density matrix renormalization group

    G. K.-L. Chan and M. Head-Gordon. “Highly correlated calculations with a polynomial cost al- gorithm: A study of the density matrix renormalization group”. In:J. Chem. Phys.116.11 (Mar. 2002), pp. 4462–4476.issn: 0021-9606.doi:10.1063/1.1449459. eprint:https://pubs.aip. org / ...

    2002 doi

  52. [60]

    Self-consistent embedding of density-matrix renormalization group wave- functions in a density functional environment

    T. Dresselhaus et al. “Self-consistent embedding of density-matrix renormalization group wave- functions in a density functional environment”. In:J. Chem. Phys.142 (2015), p. 044111

    2015

  53. [61]

    Second-order self-consistent-field density-matrix renormalization group

    Y. Ma, S. Knecht, S. Keller, and M. Reiher. “Second-order self-consistent-field density-matrix renormalization group”. In:J. Chem. Theory Comput.13 (2017), pp. 2533–2549

    2017

  54. [62]

    Low communication high performance ab initio density matrix renormalization group algorithms

    H. Zhai and G. K.-L. Chan. “Low communication high performance ab initio density matrix renormalization group algorithms”. In:J. Chem. Phys.154.22 (June 2021), p. 224116.issn: 0021- 9606.doi:10.1063/5.0050902. eprint:https://pubs.aip.org/aip/jcp/article-pdf/doi/ 10.1063/5.0050...

    2021 doi

  55. [63]

    New Density Matrix Renormalization Group Approaches for Strongly Correlated Systems Coupled with Large Environments

    Y. Xu, Y. Cheng, Y. Song, and H. Ma. “New Density Matrix Renormalization Group Approaches for Strongly Correlated Systems Coupled with Large Environments”. In:J. Chem. Theory Comput. 19.15 (2023). PMID: 37471519, pp. 4781–4795.doi:10.1021/acs.jctc.2c01316. eprint:https: //doi....

    2023 doi

  56. [65]

    Low-energy spectrum of iron–sulfur clus- ters directly from many-particle quantum mechanics

    S. Sharma, K. Sivalingam, F. Neese, and G. K.-L. Chan. “Low-energy spectrum of iron–sulfur clus- ters directly from many-particle quantum mechanics”. In:Nat. Chem.6.10 (Oct. 2014), pp. 927– 933.issn: 1755-4349.doi:10.1038/nchem.2041.url:https://doi.org/10.1038/nchem.2041

    2014 doi

  57. [66]

    Entangled quantum electronic wavefunctions of the Mn4CaO5 cluster in photosystem II

    Y. Kurashige, G. K.-L. Chan, and T. Yanai. “Entangled quantum electronic wavefunctions of the Mn4CaO5 cluster in photosystem II”. en. In:Nat. Chem.5.8 (June 2013), pp. 660–666

    2013

  58. [67]

    Four-component density matrix renormalization group

    S. Knecht, Ö. Legeza, and M. Reiher. “Four-component density matrix renormalization group.” In:J. Chem. Phys.140 (2014), p. 041101

    2014

  59. [68]

    An efficient relativistic density-matrix renormalization group implementation in a matrix-product operator formulation

    S. Battaglia, S. Keller, and S. Knecht. “An efficient relativistic density-matrix renormalization group implementation in a matrix-product operator formulation”. In:J. Chem. Theory Comput. 14 (2018), pp. 2353–2369

    2018

  60. [70]

    CorrelatedDirac–Coulomb–Breitmulticonfigurationalself-consistent-fieldmeth- ods

    C.E.Hoyeretal.“CorrelatedDirac–Coulomb–Breitmulticonfigurationalself-consistent-fieldmeth- ods”. In:J. Chem. Phys.158 (2023), p. 044101.doi:10.1063/5.0133741

    2023 doi

  61. [71]

    P.-O. Löwdin. “Quantum Theory of Many-Particle Systems. I. Physical Interpretations by Means of Density Matrices, Natural Spin-Orbitals, and Convergence Problems in the Method of Config- urational Interaction”. In:Phys. Rev.97 (1955), pp. 1474–1489

    1955

  62. [72]

    On the choice of active space orbitals in MCSCF calculations

    D. M. Rogers et al. “On the choice of active space orbitals in MCSCF calculations”. In:J. Mol. Struct. THEOCHEM434.1 (1998), pp. 239–245.issn: 0166-1280.doi:https://doi.org/10. 1016/S0166- 1280(98)00123- 7.url:https://www.sciencedirect.com/science/article/ pii/S0166128098001237

    1998

  63. [73]

    Comparison of Methods for Active Orbital Selection in Multiconfigurational Calculations

    Z. Tóth and P. Pulay. “Comparison of Methods for Active Orbital Selection in Multiconfigurational Calculations”. In:J. Chem. Theory Comput.16.12 (2020). PMID: 33170653, pp. 7328–7341.doi: 10.1021/acs.jctc.0c00123. eprint:https://doi.org/10.1021/acs.jctc.0c00123.url: https://do...

    2020 doi

  64. [74]

    Automatic Selection of an Active Space for Calculating Electronic Excitation Spectra by MS-CASPT2 or MC-PDFT

    J. J. Bao, S. S. Dong, L. Gagliardi, and D. G. Truhlar. “Automatic Selection of an Active Space for Calculating Electronic Excitation Spectra by MS-CASPT2 or MC-PDFT”. In:J. Chem. Theory Comput.14.4 (2018). PMID: 29486125, pp. 2017–2025.doi:10.1021/acs.jctc.8b00032. eprint: ht...

    2018 doi

  65. [75]

    Multiconfiguration Pair-Density Functional Theory

    G. Li Manni et al. “Multiconfiguration Pair-Density Functional Theory”. en. In:J. Chem. Theory Comput.10.9 (Sept. 2014), pp. 3669–3680

    2014

  66. [76]

    Correction to multiconfiguration pair-density functional theory

    G. Li Manni et al. “Correction to multiconfiguration pair-density functional theory”. en. In:J. Chem. Theory Comput.12.1 (Jan. 2016), p. 458

    2016

  67. [77]

    Active Space Selection Based on Natural Orbital Occupation Num- bers from n-Electron Valence Perturbation Theory

    A. Khedkar and M. Roemelt. “Active Space Selection Based on Natural Orbital Occupation Num- bers from n-Electron Valence Perturbation Theory”. In:J. Chem. Theory Comput.15.6 (2019). PMID: 31059643, pp. 3522–3536.doi:10.1021/acs.jctc.8b01293. eprint:https://doi.org/ 10.1021/acs...

    2019 doi

  68. [78]

    Extending the ASS1ST Active Space Selection Scheme to Large Molecules and Excited States

    A. Khedkar and M. Roemelt. “Extending the ASS1ST Active Space Selection Scheme to Large Molecules and Excited States”. In:J. Chem. Theory Comput.16.8 (2020). PMID: 32644789, pp. 4993–5005.doi:10.1021/acs.jctc.0c00332. eprint:https://doi.org/10.1021/acs. jctc.0c00332.url:https:...

    2020 doi

  69. [79]

    iCAS: Imposed Automatic Selection and Localization of Complete Active Spaces

    Y. Lei, B. Suo, and W. Liu. “iCAS: Imposed Automatic Selection and Localization of Complete Active Spaces”. In:J. Chem. Theory Comput.17.8 (2021), pp. 4846–4859.doi:10.1021/acs. jctc.1c00456. 23

    2021 doi

  70. [80]

    Corresponding Active Orbital Spaces along Chemical Reaction Paths

    M. Bensberg and M. Reiher. “Corresponding Active Orbital Spaces along Chemical Reaction Paths”. In:J. Phys. Chem. Lett14.8 (2023). PMID: 36802629, pp. 2112–2118.doi:10 . 1021 / acs . jpclett . 2c03905. eprint:https : / / doi . org / 10 . 1021 / acs . jpclett . 2c03905.url: htt...

    2023 doi

  71. [81]

    A Ranked-Orbital Approach to Select Active Spaces for High- Throughput Multireference Computation

    D. S. King and L. Gagliardi. “A Ranked-Orbital Approach to Select Active Spaces for High- Throughput Multireference Computation”. In:Journal of Chemical Theory and Computation17.5 (2021), pp. 2817–2831.doi:10.1021/acs.jctc.1c00037

    2021 doi

  72. [82]

    Variational Active Space Selection with Multiconfig- uration Pair-Density Functional Theory

    D. S. King, D. G. Truhlar, and L. Gagliardi. “Variational Active Space Selection with Multiconfig- uration Pair-Density Functional Theory”. In:Journal of Chemical Theory and Computation19.22 (2023), pp. 8118–8128.doi:10.1021/acs.jctc.3c00792

    2023 doi

  73. [83]

    iCISCF: An Iterative Configuration Interaction-Based Multiconfigurational Self-Consistent Field Theory for Large Active Spaces

    Y. Guo, N. Zhang, Y. Lei, and W. Liu. “iCISCF: An Iterative Configuration Interaction-Based Multiconfigurational Self-Consistent Field Theory for Large Active Spaces”. In:Journal of Chem- ical Theory and Computation17.12 (2021), pp. 7545–7561.doi:10.1021/acs.jctc.1c00781

    2021 doi

  74. [84]

    Quantum Information-Assisted Complete Active Space Optimization (QICAS)

    L. Ding, S. Knecht, and C. Schilling. “Quantum Information-Assisted Complete Active Space Optimization (QICAS)”. In:The Journal of Physical Chemistry Letters14.49 (2023), pp. 11022– 11029.doi:10.1021/acs.jpclett.3c02536

    2023 doi

  75. [85]

    R. G. Shirazi, A. Zech, P. Pinski, and V. V. Rybkin.Performance of Automatic Active Space Selection for Electronic Excitation Energies. 2025. arXiv:2511.05732 [physics.chem-ph].url: https://arxiv.org/abs/2511.05732

    2025

  76. [86]

    Szabo and N

    A. Szabo and N. S. Ostlund.Modern Quantum Chemistry: Introduction to Advanced Electronic Structure Theory. First. Mineola: Dover Publications, Inc., 1996

    1996

  77. [87]

    Self-Consistent Equations Including Exchange and Correlation Effects

    W. Kohn and L. J. Sham. “Self-Consistent Equations Including Exchange and Correlation Effects”. In:Phys. Rev.140 (4A Nov. 1965), A1133–A1138.doi:10 . 1103 / PhysRev . 140 . A1133.url: https://link.aps.org/doi/10.1103/PhysRev.140.A1133

    1965 doi

  78. [88]

    Optimizing the Density-Matrix Renormalization Group Method Using Quantum Information Entropy

    Ö. Legeza and J. Sólyom. “Optimizing the Density-Matrix Renormalization Group Method Using Quantum Information Entropy”. In:Phys. Rev. B68 (2003), p. 195116.doi:10.1103/PhysRevB. 68.195116

    2003 doi

  79. [89]

    Measuring Orbital Interaction Using Quantum Informa- tion Theory

    J. Rissler, R. M. Noack, and S. R. White. “Measuring Orbital Interaction Using Quantum Informa- tion Theory”. In:Chem. Phys.323 (2006), pp. 519–531.doi:10.1016/j.chemphys.2005.10.018

    2006 doi

  80. [90]

    Orbital Entanglement in Quantum Chemistry

    K. Boguslawski and P. Tecmer. “Orbital Entanglement in Quantum Chemistry”. In:Int. J. Quan- tum Chem.115 (2015), pp. 1289–1295

    2015

  81. [91]

    Measuring Multi-Configurational Character by Orbital Entanglement

    C. J. Stein and M. Reiher. “Measuring Multi-Configurational Character by Orbital Entanglement”. In:Mol. Phys.115 (2017), pp. 2110–2119.doi:10.1080/00268976.2017.1288934

    2017

  82. [92]

    Introduction: Theoretical Modeling of Excited State Processes

    S. Matsika and A. I. Krylov. “Introduction: Theoretical Modeling of Excited State Processes”. In:Chem. Rev.118.15 (2018), pp. 6925–6926.doi:10 . 1021 / acs . chemrev . 8b00436. eprint: https://doi.org/10.1021/acs.chemrev.8b00436.url:https://doi.org/10.1021/acs. chemrev.8b00436

    2018 doi

  83. [93]

    Block2: A Comprehensive Open Source Framework to Develop and Apply State- Of-The-Art DMRG Algorithms in Electronic Structure and Beyond

    H. Zhai et al. “Block2: A Comprehensive Open Source Framework to Develop and Apply State- Of-The-Art DMRG Algorithms in Electronic Structure and Beyond”. In:J. Chem. Phys.159.23 (2023)

    2023

  84. [94]

    Interfacing relativistic and nonrelativistic methods. I. Normalized elimination of the small component in the modified Dirac equation

    K. G. Dyall. “Interfacing relativistic and nonrelativistic methods. I. Normalized elimination of the small component in the modified Dirac equation”. In:The Journal of Chemical Physics106.23 (June 1997), pp. 9618–9626.issn: 0021-9606.doi:10.1063/1.473860. eprint:https://pubs. ...

    1997 doi

  85. [95]

    Quasirelativistic theory equivalent to fully relativistic theory

    W. Kutzelnigg and W. Liu. “Quasirelativistic theory equivalent to fully relativistic theory”. In: The Journal of Chemical Physics123.24 (Dec. 2005), p. 241102.issn: 0021-9606.doi:10.1063/ 1.2137315. eprint:https://pubs.aip.org/aip/jcp/article-pdf/doi/10.1063/1.2137315/ 1351339...

    2005 doi

  86. [96]

    Multi-configurationtime-dependentdensity-functional theory based on range separation

    E.Fromager,S.Knecht,andH.J.A.Jensen.“Multi-configurationtime-dependentdensity-functional theory based on range separation”. In:The Journal of Chemical Physics138.8 (Feb. 2013).issn: 1089-7690.doi:10.1063/1.4792199.url:http://dx.doi.org/10.1063/1.4792199. 24

    2013 doi

  87. [97]

    Electronic Absorption Spectrum of Ferrocene

    A. T. Armstrong, F. Smith, E. Elder, and S. P. McGlynn. “Electronic Absorption Spectrum of Ferrocene”. In:The Journal of Chemical Physics46.11 (June 1967), pp. 4321–4328.issn: 0021- 9606.doi:10.1063/1.1840547. eprint:https://pubs.aip.org/aip/jcp/article-pdf/46/11/ 4321/1885097...

    1967 doi

  88. [98]

    Electronic structure of metallocenes

    H. B. Gray, Y. S. Sohn, and N. Hendrickson. “Electronic structure of metallocenes”. In:Journal of the American Chemical Society93 (1971), pp. 3603–3612.url:https://api.semanticscholar. org/CorpusID:93102585

    1971

  89. [99]

    The Golden Age of Thermally Activated Delayed Fluorescence Materials: Design and Exploitation

    J. M. Dos Santos et al. “The Golden Age of Thermally Activated Delayed Fluorescence Materials: Design and Exploitation”. In:Chemical Reviews124.24 (2024). PMID: 39666979, pp. 13736–14110. doi:10 . 1021 / acs . chemrev . 3c00755. eprint:https : / / doi . org / 10 . 1021 / acs ....

    2024 doi

  90. [100]

    Ultrapure Blue Thermally Activated Delayed Fluorescence Molecules: Effi- cient HOMO–LUMO Separation by the Multiple Resonance Effect

    T. Hatakeyama et al. “Ultrapure Blue Thermally Activated Delayed Fluorescence Molecules: Effi- cient HOMO–LUMO Separation by the Multiple Resonance Effect”. In:Advanced Materials28.14 (2016), pp. 2777–2781.doi:10.1002/adma.201505491

    2016 doi

  91. [101]

    High-performance blue OLED using multiresonance thermally activated delayed fluorescence host materials containing silicon atoms

    D. Park et al. “High-performance blue OLED using multiresonance thermally activated delayed fluorescence host materials containing silicon atoms”. In:Nature Communications14.1 (Sept. 11, 2023), p. 5589.doi:10.1038/s41467-023-41440-1.url:https://doi.org/10.1038/s41467- 023-41440-1

    2023 doi

  92. [102]

    Solution-Processable Pure Green Thermally Activated Delayed Fluorescence Emit- ter Based on the Multiple Resonance Effect

    N. Ikeda et al. “Solution-Processable Pure Green Thermally Activated Delayed Fluorescence Emit- ter Based on the Multiple Resonance Effect”. In:Advanced Materials32.40 (2020), p. 2004072. doi:https://doi.org/10.1002/adma.202004072. eprint:https://advanced.onlinelibrary. wiley ...

    2020 doi

  93. [103]

    Second-order Mo/ller–Plesset pertur- bation theory as a configuration and orbital generator in multiconfiguration self-consistent field calculations

    H. J. A. Jensen, P. Jo/rgensen, H. Ågren, and J. Olsen. “Second-order Mo/ller–Plesset pertur- bation theory as a configuration and orbital generator in multiconfiguration self-consistent field calculations”. In:The Journal of Chemical Physics88.6 (Mar. 1988), pp. 3834–3839.iss...

    1988 doi

  94. [105]

    Frozen natural orbitals: Systematic basis set truncation for coupled-cluster theory

    A. G. Taube and R. J. Bartlett. “Frozen natural orbitals: Systematic basis set truncation for coupled-cluster theory”. In:Collect. Czechoslov. Chem. Commun70.6 (2005), pp. 837–850.doi: https://doi.org/10.1135/cccc20050837.url:http://cccc.uochb.cas.cz/70/6/0837/

    2005 doi

  95. [106]

    Frozen natural orbital coupled-cluster theory: Forces and ap- plication to decomposition of nitroethane

    A. G. Taube and R. J. Bartlett. “Frozen natural orbital coupled-cluster theory: Forces and ap- plication to decomposition of nitroethane”. In:J. Chem. Phys.128.16 (Apr. 2008), p. 164101. issn: 0021-9606.doi:10.1063/1.2902285. eprint:https://pubs.aip.org/aip/jcp/article- pdf/do...

    2008 doi

  96. [107]

    Scaling up electronic structure calculations on quantum computers: The frozen natural orbital based method of increments

    P. Verma et al. “Scaling up electronic structure calculations on quantum computers: The frozen natural orbital based method of increments”. In:J. Chem. Phys.155.3 (July 2021), p. 034110. issn: 0021-9606.doi:10.1063/5.0054647. eprint:https://pubs.aip.org/aip/jcp/article- pdf/do...

    2021 doi

  97. [108]

    One-Step Borylation of 1,3-Diaryloxybenzenes Towards Efficient Materials for Organic Light-Emitting Diodes

    H. Hirai et al. “One-Step Borylation of 1,3-Diaryloxybenzenes Towards Efficient Materials for Organic Light-Emitting Diodes”. In:Angewandte Chemie International Edition54.46 (2015), pp. 13581–13585.doi:https : / / doi . org / 10 . 1002 / anie . 201506335. eprint:https : / / on...

    2015 doi

  98. [109]

    Transition Metal Complexes and Photodynamic Therapy from a Tumor-Centered Approach: Challenges, Opportunities, and Highlights from the Development of TLD1433

    S. Monro et al. “Transition Metal Complexes and Photodynamic Therapy from a Tumor-Centered Approach: Challenges, Opportunities, and Highlights from the Development of TLD1433”. In: Chem. Rev.119.2 (2019), pp. 797–828.doi:10 . 1021 / acs . chemrev . 8b00211. eprint:https : //do...

    2019 doi

  99. [110]

    Increased Lipophilicity of Halogenated Ruthenium(II) Polypyridyl Complexes Leads to Decreased Phototoxicity in-vitro when Used as Photosensitizers for Photodynamic Therapy

    S. Roy et al. “Increased Lipophilicity of Halogenated Ruthenium(II) Polypyridyl Complexes Leads to Decreased Phototoxicity in-vitro when Used as Photosensitizers for Photodynamic Therapy”. In:Chem. Bio. Chem.21.20 (2020), pp. 2966–2973.doi:https : / / doi . org / 10 . 1002 / c...

    2020 doi

  100. [111]

    Time-Dependent Density Functional Response Theory for Molecules

    M. E. Casida. “Time-Dependent Density Functional Response Theory for Molecules”. In:Re- cent Advances in Density Functional Methods. World Scientific, 1995, pp. 155–192.doi:10 . 1142 / 9789812830586 _ 0005. eprint:https : / / www . worldscientific . com / doi / pdf / 10 . 1142...

    1995

  101. [112]

    Time-dependent density-functional theory for molecules and molecular solids

    M. E. Casida. “Time-dependent density-functional theory for molecules and molecular solids”. In:J. Mol. Struct.: THEOCHEM914.1 (2009). Time-dependent density-functional theory for molecules and molecular solids, pp. 3–18.issn: 0166-1280.doi:https : / / doi . org / 10 . 1016 / ...

    2009

  102. [113]

    Density-Functional Theory for Time-Dependent Systems

    E. Runge and E. K. U. Gross. “Density-Functional Theory for Time-Dependent Systems”. In: Phys. Rev. Lett.52 (12 Mar. 1984), pp. 997–1000.doi:10 . 1103 / PhysRevLett . 52 . 997.url: https://link.aps.org/doi/10.1103/PhysRevLett.52.997

    1984 doi

  103. [114]

    Theoretical Exploration of Type I/Type II Dual Photoreactivity of Promising Ru(II) Dyads for PDT Approach

    M. E. Alberto, J. Pirillo, N. Russo, and C. Adamo. “Theoretical Exploration of Type I/Type II Dual Photoreactivity of Promising Ru(II) Dyads for PDT Approach”. In:Inorganic Chemistry 55.21 (Nov. 2016), pp. 11185–11192.issn: 0020-1669.doi:10.1021/acs.inorgchem.6b01782. url:http...

    2016 doi

  104. [115]

    RASPT2/RASSCF vs Range-Separated/Hybrid DFT Methods: Assessing the Excited States of a Ru(II)bipyridyl Complex

    D. Escudero and L. González. “RASPT2/RASSCF vs Range-Separated/Hybrid DFT Methods: Assessing the Excited States of a Ru(II)bipyridyl Complex”. In:J. Chem. Theory Comput.8.1 (2012). PMID: 26592882, pp. 203–213.doi:10.1021/ct200640q. eprint:https://doi.org/10. 1021/ct200640q.url...

    2012 doi

  105. [116]

    An adaptive variational algorithm for exact molecular simulations on a quantum computer

    H. R. Grimsley, S. E. Economou, E. Barnes, and N. J. Mayhall. “An adaptive variational algorithm for exact molecular simulations on a quantum computer”. In:Nature Communications10.1 (July 2019).issn: 2041-1723.doi:10.1038/s41467- 019- 10988- 2.url:http://dx.doi.org/10. 1038/s4...

    2019 doi

  106. [118]

    Quantum Calculation of Molecular Energies and Energy Gradients in Solution by a Conductor Solvent Model

    V. Barone and M. Cossi. “Quantum Calculation of Molecular Energies and Energy Gradients in Solution by a Conductor Solvent Model”. In:J. Phys. Chem. A.102.11 (Mar. 1998), pp. 1995– 2001.issn: 1089-5639.doi:10.1021/jp9716997.url:https://doi.org/10.1021/jp9716997

    1998 doi

  107. [119]

    The Variational Quantum Eigensolver: A review of methods and best practices

    J. Tilly et al. “The Variational Quantum Eigensolver: A review of methods and best practices”. In:Phys. Rep.986 (2022). The Variational Quantum Eigensolver: a review of methods and best practices, pp. 1–128.issn: 0370-1573.doi:https://doi.org/10.1016/j.physrep.2022.08.003. url...

    2022 doi

  108. [120]

    A. Y. Kitaev.Quantum measurements and the Abelian Stabilizer Problem. 1995. arXiv:quant- ph/9511026 [quant-ph].url:https://arxiv.org/abs/quant-ph/9511026

    1995

  109. [121]

    M. A. Nielsen and I. L. Chuang.Quantum Computation and Quantum Information: 10th Anniver- sary Edition. Cambridge University Press, 2010. [122]Q4Bio Perspective Paper.2025.url:https://algorithmiq.fi/publications/Q4Bio_Perspective_ Paper.pdf

    2010

  110. [123]

    Quantum correlations in molecules: from quantum resourcing to chemical bonding

    L. Ding, S. Knecht, Z. Zimborás, and C. Schilling. “Quantum correlations in molecules: from quantum resourcing to chemical bonding”. In:Quantum Sci. Technol.8.1 (Dec. 2022), p. 015015. doi:10.1088/2058-9565/aca4ee.url:https://dx.doi.org/10.1088/2058-9565/aca4ee

    2022 doi

  111. [124]

    F.A.Evangelista.Mutual Correlation.2025.arXiv:2506.07344 [physics.chem-ph].url:https: //arxiv.org/abs/2506.07344. 26

    2025 arXiv

  112. [125]

    Golub, A

    P. Golub, A. Antalik, L. Veis, and J. Brabec.Automatic selection of active spaces for strongly cor- related systems using machine learning algorithms. 2020. arXiv:2011.14715 [physics.chem-ph]. url:https://arxiv.org/abs/2011.14715

    2020 arXiv

  113. [126]

    HQSQuantumSimulations.Active-Space Finder.https://github.com/HQSquantumsimulations/ ActiveSpaceFinder. 2025

    2025

  114. [127]

    Zhao et al.Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers

    L. Zhao et al.Quantum-Classical Auxiliary Field Quantum Monte Carlo with Matchgate Shadows on Trapped Ion Quantum Computers. 2025. arXiv:2506 . 22408 [quant-ph].url:https : / / arxiv.org/abs/2506.22408

    2025 arXiv

  115. [128]

    Avogadro: an advanced semantic chemical editor, visualization, and analysis platform

    M. D. Hanwell et al. “Avogadro: an advanced semantic chemical editor, visualization, and analysis platform”. In:J. Cheminform4.1 (Aug. 2012), p. 17.issn: 1758-2946.doi:10.1186/1758-2946- 4-17.url:https://doi.org/10.1186/1758-2946-4-17

    2012 doi

  116. [129]

    VESTA: a three-dimensional visualization system for electronic and structural analysis

    K. Momma and F. Izumi. “VESTA: a three-dimensional visualization system for electronic and structural analysis”. In:J. Appl. Crystallogr.41.3 (June 2008), pp. 653–658.doi:10 . 1107 / S0021889808012016.url:https://doi.org/10.1107/S0021889808012016

    2008 doi

  117. [130]

    Claudino.PsiEmb.https://github.com/danclaudino/PsiEmbed

    D. Claudino.PsiEmb.https://github.com/danclaudino/PsiEmbed. 2020

    2020

  118. [131]

    Density-functional thermochemistry. III. The role of exact exchange

    A. D. Becke. “Density-functional thermochemistry. III. The role of exact exchange”. In:J. Chem. Phys.98.7 (Apr. 1993), pp. 5648–5652.issn: 0021-9606.doi:10.1063/1.464913. eprint:https: //pubs.aip.org/aip/jcp/article- pdf/98/7/5648/19277469/5648\_1\_online.pdf.url: https://doi....

    1993 doi

  119. [132]

    Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields

    P. J. Stephens, F. J. Devlin, C. F. Chabalowski, and M. J. Frisch. “Ab Initio Calculation of Vibrational Absorption and Circular Dichroism Spectra Using Density Functional Force Fields”. In:J. Phys. Chem98.45 (1994), pp. 11623–11627.doi:10.1021/j100096a001. eprint:https: //doi...

    1994 doi

  120. [133]

    Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy

    F. Weigend and R. Ahlrichs. “Balanced basis sets of split valence, triple zeta valence and quadruple zeta valence quality for H to Rn: Design and assessment of accuracy”. In:Phys. Chem. Chem. Phys.7 (18 2005), pp. 3297–3305.doi:10.1039/B508541A.url:http://dx.doi.org/10.1039/ B508541A

    2005 doi

  121. [134]

    Energy-adjustedab initio pseudopotentials for the second and third row transition elements

    D. Andrae et al. “Energy-adjustedab initio pseudopotentials for the second and third row transition elements”. In:Theor. Chim. Acta77.2 (Mar. 1990), pp. 123–141

    1990

  122. [135]

    The ORCA program system

    F. Neese. “The ORCA program system”. In:WIRES Comput. Molec. Sci.2.1 (2012), pp. 73–78. doi:10.1002/wcms.81

    2012 doi

  123. [136]

    Density matrix averaged atomic natural orbital (ANO) basis sets for correlated molecular wave functions

    K. Pierloot, B. Dumez, P.-O. Widmark, and B. O. Roos. “Density matrix averaged atomic natural orbital (ANO) basis sets for correlated molecular wave functions”. In:Theor. Chim. Acta90.2 (Jan. 1995), pp. 87–114

    1995

  124. [137]

    New relativistic ANO basis sets for transition metal atoms

    B. O. Roos et al. “New relativistic ANO basis sets for transition metal atoms”. en. In:J. Phys. Chem. A109.29 (July 2005), pp. 6575–6579

    2005

  125. [138]

    Use of approximate integrals in ab initio theory. An application in MP2 energy calculations

    M. Feyereisen, G. Fitzgerald, and A. Komornicki. “Use of approximate integrals in ab initio theory. An application in MP2 energy calculations”. In:Chem. Phys. Lett.208.5 (1993), pp. 359–363. issn: 0009-2614.doi:https : / / doi . org / 10 . 1016 / 0009 - 2614(93 ) 87156 - W.url...

    1993

  126. [139]

    M. J. Frisch et al.Gaussian~16 Revision C.01. Gaussian Inc. Wallingford CT. 2016

    2016

  127. [140]

    AssessmentofthePerdew–Burke–Ernzerhofexchange-correlation functional

    M.ErnzerhofandG.E.Scuseria.“AssessmentofthePerdew–Burke–Ernzerhofexchange-correlation functional”. In:J. Chem. Phys.110.11 (Mar. 1999), pp. 5029–5036.issn: 0021-9606.doi:10.1063/ 1.478401. eprint:https://pubs.aip.org/aip/jcp/article- pdf/110/11/5029/19111374/ 5029\_1\_online.p...

    1999 doi

  128. [141]

    Toward reliable density functional methods without adjustable param- eters: The PBE0 model

    C. Adamo and V. Barone. “Toward reliable density functional methods without adjustable param- eters: The PBE0 model”. In:J. Chem. Phys.110.13 (Apr. 1999), pp. 6158–6170.issn: 0021-9606. doi:10.1063/1.478522. eprint:https://pubs.aip.org/aip/jcp/article-pdf/110/13/6158/ 19068890...

    1999 doi

  129. [142]

    Property-optimized Gaussian basis sets for molecular response calculations

    D. Rappoport and F. Furche. “Property-optimized Gaussian basis sets for molecular response calculations”. In:J. Chem. Phys.133 (2010), p. 134105.doi:10.1063/1.3484283. 27

    2010 doi

  130. [143]

    Entanglement Measures for Single- and Multireference Correlation Effects

    K. Boguslawski, P. Tecmer, Ö. Legeza, and M. Reiher. “Entanglement Measures for Single- and Multireference Correlation Effects”. In:J. Phys. Chem. Lett.3 (2012), pp. 3129–3135.doi:10. 1021/jz301319v. 28 Supplementary Information AEGISS - Atomic orbital and Entropy-based Guided...

    2012

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.