Quantum Chemistry

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Quantum chemistry studies how electrons determine the structure, energy, and behavior of molecules and materials, helping explain reactions and predict chemical properties. Its main approaches include density functional theory, wavefunction methods such as coupled cluster and quantum Monte Carlo, and configuration interaction, which trade computational cost against accuracy. Machine-learning models increasingly predict densities, wavefunctions, energies, and forces, with tests spanning datasets such as QM9 and QMugs, bond breaking, excited-state dynamics, and periodic materials. The central challenge is making these methods accurate and physically reliable while extending them to larger systems, unfamiliar geometries, and practical quantum hardware.

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65 papers11 papers in the last four weeks, up 83% on the four weeks before. 0.1% of all new papers.

Physical structure as an organizing principle for universal Hamiltonian learning
Oct 26, 2026Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang +3Computational Materials Science Physics-Informed ML
Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China · Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore · Department of Physics, McGill University, Montreal, Quebec H3A 2T8, Canada +4
Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
Oct 13, 2026Pavlo O. Dral, Yi-Fan Hou, Yuxinxin Chen +4Machine Learning Interatomic Potentials Transfer Learning
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 +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.
Solvating machine-learning interatomic potentials for solution-phase chemistry
Oct 12, 2026Anton Z. Ni, Joonho LeeMachine Learning Interatomic Potentials Molecular Property Prediction
Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA
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
PEACE: Covariant learning of nonadiabatic manifolds with parity-resolved Hamiltonians
Oct 7, 2026Rongzhi Gao, Shuguang Chen, Yang Zhou +3Molecular Dynamics Simulation Quantum Chemistry
Department of Chemistry, The University of Hong Kong, Pok Fu Lam, Hong Kong SAR, China · Hong Kong Quantum AI Lab, Pak Shek Kok, Hong Kong SAR, China · MattVerse Limited, Pak Shek Kok, Hong Kong SAR, China +1
Electronic Density versus Geometry for Machine-Learned Molecular Absorption Spectra
Oct 2, 2026Siddharth Dhanpal, Peter Elliott, Paolo Emilio Trevisanutto +2Molecular Property Prediction Molecular Representation Learning
Scientific Computing Department, Science and Technology Facilities Council, Rutherford Appleton Laboratory, Didcot OX11 0QX, United Kingdom. · Scientific Computing Department, Science and Technology Facilities Council, Daresbury Laboratory, Warrington, WA4 4AD, United Kingdom.
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
SQD-Agent: LLM-driven agentic framework for Quantum Chemistry workflows
Sep 30, 2026Kislaya Tiwari, Anupama RayQuantum Chemistry Agentic Workflows
Indian Institute of Technology Delhi · IBM Quantum, IBM Research
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
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
Complete Neural Electronic Initialization Accelerates Materials DFT
Sep 18, 2026Felix Ærtebjerg, Jonas Elsborg, Arghya BhowmikQuantum Chemistry Computational Materials Science
Department of Energy Conversion and Storage, Technical University of Denmark
Gradient-estimator design overcomes trainability barriers in neural-network-based variational optimization
Sep 16, 2026Yi-Ran Xue, Rui Wang, Baigeng Wang +1Variance Reduction Neural Quantum States
National Laboratory of Solid State Microstructures and Department of Physics, Nanjing University, Nanjing 210093, China · Department of Physics, University of Massachusetts, Amherst, Massachusetts 01003, USA · Collaborative Innovation Center of Advanced Microstructures, Nanjing University, Nanjing 210093, China +4
QALPA: Property-guided diffusion modeling for efficient exploration of chemical spaces of flexible molecules
Sep 15, 2026Michael Hanna, Julian Cremer, Zekiye Erarslan +1Molecular Generation 3D Molecular Generation
Faculty of Computer Science, TUD Dresden University of Technology, 01062 Dresden, Germany · Machine Learning & Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany · Center for Advanced Systems Understanding (CASUS), Conrad-Schiedt-Straße 20, Görlitz 02826, Germany +2
Latent unified smooth Hamiltonians for excited state chemistry
Sep 1, 2026David Juergens, Martin Stöhr, Andreas E. Hillers-Bendtsen +2Implicit Neural Representations Quantum Chemistry
Department of Chemistry and The PULSE Institute, Stanford University, Stanford 4305, California, USA · SLAC National Accelerator Laboratory, Menlo Park, 94025, California, USA
Sharp Mixed Spectral Barron Regularity of Coulombic Many-Electron Wave Functions
Sep 1, 2026Pingbing Ming, Hao YuQuantum Chemistry Quantum Many-Body Physics
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
Sharp Barron Regularity Results for Coulombic Many-Electron Wave Functions
Aug 23, 2026Pingbing Ming, Hao YuQuantum Many-Body Physics Quantum Chemistry
Coupled-cluster molecular properties across the main group that extrapolate beyond training size
Aug 18, 2026Wenhao He, Xu Chen, Noah Song +10Quantum Chemistry Equivariant Neural Networks
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
When do machine-learned exchange-correlation improvements inherit into density-functional tight binding?
Aug 14, 2026Can Polat, Mustafa Kurban, Erchin Serpedin +1Density Functional Theory Computational Materials Science
Department of Computer and Electrical Engineering, Texas A&M University, College Station, TX, USA · Department of Electrical and Computer Engineering, Texas A&M University at Qatar, Doha, Qatar · Department of Prosthetics and Orthotics, Ankara University, Ankara, Turkey +1
Reaction-Transformation-Aware Flow Matching for Generalizable Transition State Generation
Aug 14, 2026Kaipeng Zeng, Wenxi Zhai, Shengrui Xu +5Quantum Chemistry Molecular Generation
School of Computer Science & School of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, 200240, Shanghai, China. · Shanghai Innovation Institute, Shanghai, 200231, Shanghai, China. · School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, 230026, Anhui, China. +3
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
A Density-Matrix Framework for Electronic-Structure Analysis of Electrolytes for Lithium Batteries
Jul 28, 2026Mingkang Liu, Huize Yu, Lei ShenQuantum Chemistry Materials Science
Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore · National University of Singapore (Chongqing) Research Institute, Chongqing 401120, China · National University of Singapore Guangzhou Research Translation and Innovation Institute, Guangzhou, Guangdong 510700, China
Learning to Prepare Molecular Ground States with Transformer Models
Jul 24, 2026Alex Koziell-Pipe, Jasmine Brewer, Jem Guhit +14Quantum Chemistry Quantum State Preparation
1Quantinuum, London, UK. · 3NVIDIA Corporation, Santa Clara, CA, USA. · 2Quantinuum, Cambridge, UK. +2
An Analytically Trained Variational Surrogate for Quantum Phase Estimation on NISQ Hardware
Jul 23, 2026Mousumi Kundu, Ashish Kumar Patra, Anurag K. S. V. +4Quantum Chemistry Variational Quantum Circuits
Qclairvoyance Quantum Labs, Secunderabad, TG 500094, India. · Department of CSE(AI & ML), School of Engineering, Dayananda Sagar University, Bengaluru, KA 562112, India. · The University of Arizona, Tucson, AZ 85721, USA. +1
Machine-Learned Compact Subspace Generation for Quantum Selected Configuration Interaction within Density Matrix Embedding Framework
Jul 22, 2026Ashish Kumar Patra, Anurag K. S. V., Ruchika Bhat +3Quantum Chemistry Restricted Boltzmann Machines
1Qclairvoyance Quantum Labs, Secunderabad, TG 500094, India. · 2The University of Arizona, Tucson, AZ 85721, USA. · 3Indian Institute of Technology Bombay, Mumbai, MH 400076, India.
Nuclear Quantum Effects as a Denoising Problem
Jul 22, 2026Weizhou Wang, Jonathan Weare, Aaron R. DinnerDiffusion-Based Denoising Quantum Chemistry
Department of Chemistry, University of Chicago, Chicago, Illinois 60637, USA · Courant Institute of Mathematical Sciences, New York University, New York, New York 10012, USA · James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA
Enerzyme: A Framework for Efficient Training of Reactive Neural Network Potentials for Enzyme Catalysis with Application to Methyltransferases
Jul 1, 2026Weiliang Luo, Heather J. KulikMachine Learning Interatomic Potentials Quantum Chemistry
Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA · Department of Chemistry, Massachusetts Institute of Technology, Cambridge, MA 02139, USA

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Physical structure as an organizing principle for universal Hamiltonian learning

Mingkang Liu, Zhanghao Zhouyin, Guanghui Wang, Daye Zheng+2

Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou 215123, China · Department of Mechanical Engineering, National University of Singapore, 9 Engineering Drive 1, Singapore 117575, Singapore · Department of Physics, McGill University, Montreal, Quebec H3A 2T8, Canada+4

Computational Materials Science Physics-Informed ML Quantum Chemistry Materials Property Prediction Equivariant Representation Learning

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Density functional theory (DFT) gives the electronic structure of materials, but its cost limits large-supercell calculations and materials screening. Machine-learned Hamiltonians bypass the self-consistent cycle but are usually system-specific; universal ones could cover new compositions and disordered solids. Such models must generalize across chemistry and keep downstream properties physical, while staying inexpensive for large cells. Here we introduce UniTB, a universal E(3) equivariant Hamiltonian representation framework that integrates physical priors rooted in density-functional tight-binding theory with deep learning. UniTB's network, which corrects a prior Hamiltonian, generalizes across chemistry through a mixture of experts routed by that prior and a charge-equilibration head as in self-consistent-charge tight binding. A projection bounds band shifts from the prior, suppressing spurious states and keeping downstream properties physical. We provide the fastest SO(2) tensor-product GPU kernels for the network. The variant UniTB-Flash assembles its prior from two-center tables at

O(N)

cost for

N

atoms, against

O(N\log N)

on a grid. Trained on 29,242 crystals, UniTB reaches a 0.48 meV matrix-element error on nonmagnetic test crystals spanning 68 elements. Without amorphous training data, both models agree with DFT optics and conductivity of amorphous Ge-Sb-Te. UniTB-Flash ranks candidates with no significant difference from UniTB, whose property values are more accurate; together they screen 16,195 crystals for five application domains of industrial relevance.