SLayerGen generates crystals invariant to any space or layer group via autoregressive lattice and Wyckoff sampling plus equivariant diffusion, achieving gains over bulk models on diperiodic materials after correcting a prior loss inconsistency for hexagonal groups.
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2026 17representative citing papers
MP2SS reduces finite-size errors in periodic MP2 to millihartree accuracy at coarser k-point meshes for gapped systems via auxiliary function subtraction.
Constraint-aware neural networks clone known semilocal XC functionals more accurately in self-consistent calculations, transfer well from molecules to solids, and outperform unconstrained models across multiple tests.
LitXBench shows frontier LLMs like Gemini 3.1 Pro Preview outperform extraction pipelines by 0.37 F1 because they link measurements to processing steps rather than just compositions.
AQVolt26 is a new high-temperature halide dataset that improves universal ML interatomic potentials for distorted configurations while showing that near-equilibrium relaxation data is not universally helpful.
Electrospinning-Data.org is a FAIR data platform that organizes electrospinning experiments into a structured, failure-inclusive corpus to enable predictive modeling and inverse design of nanofiber morphologies.
RADAR-PD introduces a modality-aware ML system that generates phase hypotheses from elemental constraints and performs recursive multiphase analysis with physics-constrained verification on experimental diffraction data.
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
A facet-resolved adsorption energy distribution method with ML force fields identifies active and methanol-selective alloy nanocatalyst surfaces for CO2 hydrogenation.
aim2dat is a new Python toolkit providing interfaces for database queries, high-throughput DFT workflows, and machine learning integration to handle large material datasets.
Multi-fidelity bandits screen 529 ZnO co-dopants with 81% fewer DFT calls, identify Y2Cu2 co-doped ZnO (1.84 eV) as optimal for visible-light band gaps, and release all 583 calculations plus code.
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
An ontology-aligned framework for atomistic simulations that integrates over 750,000 triples to enable interoperable data querying and automated provenance tracking.
Electrostatic screening of stoichiometric slabs and surface dipoles predicts stable facets and morphologies in ionic crystals faster than DFT, with predictions matching experiments on tested materials.
Binding sfTA produces bilayer binding correlation energies closer to twist-averaged CCSD than standard sfTA by incorporating binding interactions into twist-angle selection.
SA-ADAPT reaches near-CASSCF accuracy for a multiconfigurational surface chemistry benchmark using far fewer operators than SA-fUCCSD, with a modified selection scheme speeding convergence.
DFT calculations show guest atom ionization potential controls stability and rattler motion in A8T27Pn19 clathrates, spin-orbit coupling matters for heavy elements, and synthesis yields new compounds but misses the target phases.
citing papers explorer
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SLayerGen: a Crystal Generative Model for all Space and Layer Groups
SLayerGen generates crystals invariant to any space or layer group via autoregressive lattice and Wyckoff sampling plus equivariant diffusion, achieving gains over bulk models on diperiodic materials after correcting a prior loss inconsistency for hexagonal groups.
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Reduction of finite-size effects for second-order M{\o}ller-Plesset perturbation theory with singularity subtraction
MP2SS reduces finite-size errors in periodic MP2 to millihartree accuracy at coarser k-point meshes for gapped systems via auxiliary function subtraction.
-
Constraint-aware functional cloning for stable and transferable machine-learned density functional theory
Constraint-aware neural networks clone known semilocal XC functionals more accurately in self-consistent calculations, transfer well from molecules to solids, and outperform unconstrained models across multiple tests.
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LitXBench: A Benchmark for Extracting Experiments from Scientific Literature
LitXBench shows frontier LLMs like Gemini 3.1 Pro Preview outperform extraction pipelines by 0.37 F1 because they link measurements to processing steps rather than just compositions.
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AQVolt26: High-Temperature r$^2$SCAN Halide Dataset for Universal ML Potentials and Solid-State Batteries
AQVolt26 is a new high-temperature halide dataset that improves universal ML interatomic potentials for distorted configurations while showing that near-equilibrium relaxation data is not universally helpful.
-
Electrospinning-Data.org: A FAIR, Structured Knowledge Resource for Nanofiber Fabrication
Electrospinning-Data.org is a FAIR data platform that organizes electrospinning experiments into a structured, failure-inclusive corpus to enable predictive modeling and inverse design of nanofiber morphologies.
-
Automated multiphase identification and refinement in powder diffraction using mismatch-tolerant machine learning
RADAR-PD introduces a modality-aware ML system that generates phase hypotheses from elemental constraints and performs recursive multiphase analysis with physics-constrained verification on experimental diffraction data.
-
Fast and Accurate Prediction of Lattice Thermal Conductivity via Machine Learning Surrogates
Machine learning models, especially certain deep neural networks, can predict lattice thermal conductivity with useful accuracy across different generalization tests while being orders of magnitude faster than first-principles calculations.
-
Selectivity- and Activity-Aware Catalyst Descriptors for CO$_2$ Hydrogenation on Alloy Nanocatalysts using Machine-Learned Force Fields
A facet-resolved adsorption energy distribution method with ML force fields identifies active and methanol-selective alloy nanocatalyst surfaces for CO2 hydrogenation.
-
aim2dat: A Python infrastructure for automated ab initio material modeling and data analysis
aim2dat is a new Python toolkit providing interfaces for database queries, high-throughput DFT workflows, and machine learning integration to handle large material datasets.
-
Accelerated Dopant Screening in Oxide Semiconductors via Multi-Fidelity Contextual Bandits and a Three-Tier DFT Validation Funnel
Multi-fidelity bandits screen 529 ZnO co-dopants with 81% fewer DFT calls, identify Y2Cu2 co-doped ZnO (1.84 eV) as optimal for visible-light band gaps, and release all 583 calculations plus code.
-
Multi-Label Phase Diagram Prediction in Complex Alloys via Physics-Informed Graph Attention Networks
Physics-informed graph attention networks predict multi-phase equilibria in Ag-Bi-Cu-Sn alloys with 96% exact-set accuracy on in-domain data and strong generalization to unseen sections.
-
Ontology-based knowledge graph infrastructure for interoperable atomistic simulation data
An ontology-aligned framework for atomistic simulations that integrates over 750,000 triples to enable interoperable data querying and automated provenance tracking.
-
Accelerated Prediction of Surface Stability and Particle Morphology in Ionic Crystals via Electrostatic Screening
Electrostatic screening of stoichiometric slabs and surface dipoles predicts stable facets and morphologies in ionic crystals faster than DFT, with predictions matching experiments on tested materials.
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A Single Twist-Angle Selection Method for the Electronic Structure of Bilayer Materials
Binding sfTA produces bilayer binding correlation energies closer to twist-averaged CCSD than standard sfTA by incorporating binding interactions into twist-angle selection.
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State-Averaged Quantum Algorithms for Multiconfigurational Surface Chemistry: A Benchmark on Rh@TiO2(110)
SA-ADAPT reaches near-CASSCF accuracy for a multiconfigurational surface chemistry benchmark using far fewer operators than SA-fUCCSD, with a modified selection scheme speeding convergence.
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Stability and superstructural ordering of alkali-triel-pnictide clathrates A$_8$T$_{27}$Pn$_{19}$
DFT calculations show guest atom ionization potential controls stability and rattler motion in A8T27Pn19 clathrates, spin-orbit coupling matters for heavy elements, and synthesis yields new compounds but misses the target phases.