Computational Quantum Chemistry

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34 papers6 papers in the last four weeks, up 50% on the four weeks before. 0.0% of all new papers.

Equivariant Deep Learning in Direct-Product Space for Electronic-Structure Hamiltonian Prediction
Nov 5, 2026Shi Yin, Zujian Dai, Xinyang Pan +1Equivariant Neural Networks Equivariant Representation Learning
Institute of Artificial Intelligence, Hefei Comprehensive National Science Center · Laboratory of Quantum Information, University of Science and Technology of China
PULSES: multi-agent AI for autonomous excited-state model building and dynamics
Oct 23, 2026Fangning Ren, Lechen Dong, Xu Chen +3AI Agents for Scientific Discovery Autonomous Research Agents
Department of Chemistry, Emory University, Atlanta, GA, 30322, USA
Fitting Conventional and Neural Exchange-Correlation Functionals to Accurate Densities without Self-Consistent Fields
Oct 21, 2026Yuhao Chen, Donald G. Truhlar, Xiao HeDensity Functional Theory Computational Quantum Chemistry
Shanghai Engineering Research Center of Molecular Therapeutics and New Drug Development, Shanghai Frontiers Science Center of Molecule Intelligent Syntheses, School of Chemistry and Molecular Engineering, East China Normal University, Shanghai 200062, China · Department of Chemistry, Chemical Theory Center, and Minnesota Supercomputing Institute, University of Minnesota, Minneapolis, Minnesota 55455-0431, USA · Anhui Provincial Key Laboratory of Advanced Catalysis and Energy Materials, Anhui Ultra High Molecular Weight Polyethylene Fiber Engineering Research Center, School of Chemistry and Chemical Engineering, Anqing Normal University, Anqing 261433, China +1
All-electron machine-learned orbital-free density functional for periodic CHNO systems
Oct 13, 2026Marc K. Ickler, Andreas Hermann, Fred A. HamprechtDensity Functional Theory Computational Quantum Chemistry
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, 69120, Germany. · Centre for Science at Extreme Conditions and SUPA, School of Physics and Astronomy, University of Edinburgh, Edinburgh, EH9 3FD, United Kingdom.
Electron-Density-Informed Molecular Property Prediction Using Surrogate Functionals
Oct 9, 2026Manuel V. Klockow, Simon Wagner, Roman Remme +2Density Functional Theory Molecular Property Prediction
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University 69120 Heidelberg, Germany
NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo
Oct 9, 2026Qiao Gao, Yang Huang, Enze Hou +3Variational Monte Carlo Computational Quantum Chemistry
School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei 230026, China · University of Science and Technology of China, Hefei 230026, China · Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215213, China +5
Physics-Aligned Electronic Ground-State Learning Improves Generalization
Oct 7, 2026Eike S. Eberhard, Xaver Kainz, Viktor Kotsev +2Physics-Informed ML OOD Generalization
Technical University of Munich (TUM) · Munich Data Science Institute (MDSI) · Munich Center for Machine Learning (MCML) +1
Equivariant Flow Matching for Electron Density Prediction
Oct 2, 2026Chenxing Liang, Chengdong Wang, Yuchao Lin +2Computational Quantum Chemistry Quantum Chemistry
Department of Computer Science and Engineering, Texas A&M University · Department of Materials Science and Engineering, Texas A&M University · Department of Electrical and Computer Engineering, Texas A&M University +2
Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization
Sep 29, 2026Lizhong Fu, Jianan Wei, Wenguan Wang +1Neural Quantum States Computational Quantum Chemistry
State Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China
TMCS: Tool-Grounded Multi-Agent Reasoning for Compositional Chemical Problem Solving
Sep 28, 2026Shengqin Wang, Jie Jin, Yu Cheng +4Molecular Optimization Tool-Augmented Language Model Agents
East China Normal University, Shanghai Innovation Institute, University College London, Huawei Noah’s Ark Lab
Scaling Density Functional Theory with Gaussian Splatting
Sep 25, 2026Andrés Guzmán-Cordero, Cindy Zhang, Majdi Hassan +3Density Functional Theory Computational Quantum Chemistry
Mila - Quebec AI Institute · Université de Montréal · Princeton University +2
Learning the Kohn-Sham map with neural operators for quasi-linear scaling density functional theory
Aug 24, 2026Danish Khan, Maurice D. Hanisch, Nikolai Argatoff +3Density Functional Theory Computational Quantum Chemistry
Department of Computing and Mathematical Sciences, California Institute of Technology · Department of Mathematics, ETH Zürich · Division of Chemistry and Chemical Engineering, California Institute of Technology +1
Science Done on a Machine by a Machine: AI Agents in Computational Chemistry
Aug 19, 2026Pavlo O. Dral, Hassan Nawaz, Arif UllahAI Agents for Scientific Discovery Computational Quantum Chemistry
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, Xiamen University, Xiamen, Fujian 361005, China · Institute of Physics, Faculty of Physics, Astronomy, and Informatics, Nicolaus Copernicus University in Toruń, ul. Grudziadzka 5, 87-100 Toruń, Poland · Institute of Advanced Studies, Nicolaus Copernicus University in Toruń, ul. Wileńska 4, 87-100 Toruń, Poland +2
Coupled-cluster molecular properties across the main group that extrapolate beyond training size
Aug 18, 2026Wenhao He, Xu Chen, Noah Song +10Hamiltonian Learning Quantum Chemistry
Center for Computational Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Emory University, Atlanta, GA 30322, USA · Department of Nuclear Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA +2
Density-Functional Excited-State Gradients and Nonadiabatic Couplings on a Consumer GPU from a Contraction-DAG
Aug 6, 2026Rubén Darío GuerreroQuantum Chemistry Computational Quantum Chemistry
NeuroTechNet S.A.S., 1108831, Bogot´a, Colombia · Quantum and Computational Chemistry Group (QCCG), Universidad Nacional de Colombia, Bogot´a, Colombia
Chem World: A Large-Scale Benchmark and Physics-Informed Framework for Trustworthy Chemical Property Prediction
Jul 30, 2026Tianyou Bai, Huan Wang, Mingchen Gao +4Computational Quantum Chemistry Physics-Informed ML
1Cleer Science · Institute of Automation, Chinese Academy of Sciences · Department of Chemistry, Imperial College London +1
Bridging the NISQ and Fault-Tolerant Regimes: Generative-ML-Assisted Quantum Selected CI for Molecular Simulations
Jun 29, 2026Anurag K. S. V., Ashish Kumar Patra, Manas Mukherjee +4Quantum Chemistry Restricted Boltzmann Machines
Qclairvoyance Quantum Labs, Secunderabad, TG 500094, India · Centre for Quantum Technologies, National University of Singapore, SG 117543, Singapore · The University of Arizona, Tucson, AZ 85721, USA +1
MALOQ: Massively Accelerated Learning of Operators for Quantum Transport
Jun 27, 2026Manasa Kaniselvan, Alexander Maeder, Denghui Lu +2Quantum Chemistry High-Performance Computing
D-ITET, ETH Zurich, Zurich, Switzerland
LLM-Guided Test-Time Discovery of Quantum-Chemical Approximation Algorithms
Jun 17, 2026Masaya Hagai, Yuta Suzuki, Tomoya Murata +2Automated Algorithm Discovery Quantum Chemistry
Lattice Lab, Toyota Motor Corporation · Department of Chemistry, Graduate School of Science, Nagoya University · National Institute of Informatics
Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Jun 1, 2026Vivin Vinod, Peter ZaspelQuantum Chemistry Multifidelity Modeling
School of Mathematics and Natural Sciences, University of Wuppertal, Germany.
Scalable Inference-Time Annealing with Surrogate Likelihood Estimators
May 29, 2026Daniel Peñaherrera, Rishal Aggarwal, David Ryan KoesComputational Quantum Chemistry Generative Flow Networks
CMU-Pitt PhD Program in Computational Biology · Dept. of Computational & Systems Biology, University of Pittsburgh, Pittsburgh, PA 15260, USA
Don't Get Your Kroneckers in a Twist: Gaussian Processes on High-Dimensional Incomplete Grids
May 8, 2026Mads Greisen Højlund, August Smart Lykke-Møller, Henry Moss +1Computational Quantum Chemistry Kernel Methods
Department of Chemistry Aarhus University · School of Mathematical Sciences Lancaster University
ARMOR: An Agentic Framework for Reaction Feasibility Prediction via Adaptive Utility-aware Multi-tool Reasoning
May 8, 2026Ye Liu, Botao Yu, Xinyi Ling +2Computational Quantum Chemistry LLM Tool Use
Department of Biomedical Informatics The Ohio State University · Department of Computer Science and Engineering The Ohio State University · Division of Medicinal Chemistry and Pharmacognosy The Ohio State University +1
Generative Quantum-inspired Kolmogorov-Arnold Eigensolver
May 6, 2026Yu-Cheng Lin, Yu-Chao Hsu, I-Shan Tsai +9Quantum Generative Modeling Quantum Chemistry
Department of Electrophysics, National Yang Ming Chiao Tung University, Hsinchu, Taiwan. · National Center for High-Performance Computing, National Institutes of Applied Research, Hsinchu, Taiwan · Cross College Elite Program, National Cheng Kung University, Tainan, Taiwan +7
Towards Accelerated SCF Workflows with Equivariant Density-Matrix Learning and Analytic Refinement
Apr 29, 2026Zuriel Y. Yescas-Ramos, Andrés Álvarez-García, Huziel E. SaucedaQuantum Chemistry Equivariant Neural Networks
Instituto de Física, Universidad Nacional Autónoma de México, Cd. de México C.P. 04510, Mexico
Transferable SCF-Acceleration through Solver-Aligned Initialization Learning
Apr 23, 2026Eike S. Eberhard, Viktor Kotsev, Timm Güthle +1Quantum Chemistry Density Functional Theory
1Technical University of Munich (TUM) · 2Munich Data Science Institute (MDSI) · 3Munich Center for Machine Learning (MCML)
Surrogate Functionals for Machine-Learned Orbital-Free Density Functional Theory
Apr 22, 2026Roman Remme, Fred A. HamprechtSurrogate Modeling Quantum Chemistry
Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University
A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches
Mar 11, 2026Rohit GoswamiComputational Quantum Chemistry Bayesian Optimization
Institute IMX and Lab-COSMO, École polytechnique fédérale de Lausanne · Institute IMX and Lab-COSMO, École polytechnique fédérale de Lausanne (EPFL), Station 12, CH-1015 Lausanne, Switzerland
Tensor Hypercontraction Error Correction Using Regression
Feb 27, 2026Ishna Satyarth, Eric C. Larson, Devin A. MatthewsComputational Quantum Chemistry Quantum Chemistry
Department of Computer Science, Southern Methodist University, Dallas, TX 75275, USA · Department of Chemistry, Southern Methodist University, Dallas, TX 75275, USA
Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions
Feb 23, 2026Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9Quantum Chemistry Computational Quantum Chemistry
Department of Computer Science, University of Toronto, Sandford Fleming Building, 10 King’s College Road, ON M5S 3G4, Toronto, Canada · Vector Institute for Artificial Intelligence, 661 University Ave. Suite 710, ON M5G 1M1, Toronto, Canada · Department of Chemistry, University of Toronto, Lash Miller Chemical Laboratories, 80 St. George Street, ON M5S 3H6, Toronto, Canada +6

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Equivariant Deep Learning in Direct-Product Space for Electronic-Structure Hamiltonian Prediction

Shi Yin, Zujian Dai, Xinyang Pan, Lixin He

Institute of Artificial Intelligence, Hefei Comprehensive National Science Center · Laboratory of Quantum Information, University of Science and Technology of China

Equivariant Neural Networks Equivariant Representation Learning Computational Quantum Chemistry Materials Property Prediction

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Most existing equivariant architectures for Hamiltonian prediction propagate features in direct-sum spaces of irreducible spherical tensors, whereas the target Hamiltonian is naturally an operator block in a direct-product representation over local orbital and spin bases. This representation mismatch fragments contiguous Hamiltonian blocks, obscures direct manipulation of orbital couplings, and organizes features into irregular angular-momentum components that require numerous transformations and are poorly aligned with efficient hardware execution and memory management. To address both the representation mismatch and computational bottleneck, we propose DP-Learner, an equivariant learning framework that performs message passing directly on Hamiltonian-like operator blocks in the direct-product space. Theoretically, we propose Lie-generator-based directional embedding and invariantly weighted left--right products and contractions that preserve equivariance to SO(3), SU(2), and spatial inversions, without explicit Clebsch--Gordan decomposition or inverse reconstruction. Methodologically, we instantiate this principle as a Transformer architecture, in which Hamiltonian-like feature matrices or tensors evolve through equivariant operators in the direct-product space, while non-linear invariant scalar routing controls structured gating over matrix units, directional propagation, attention, and environment-embedded context interactions. We further construct MP-HAM-110K, a large-scale Hamiltonian dataset comprising more than 110,000 material structures covering 68 elements with both Hamiltonian and wavefunction data. Experiments on MP-HAM-110K and Materials-HAM-SOC demonstrate that DP-Learner dramatically improves Hamiltonian prediction accuracy and inference efficiency over the state-of-the-art method.