Overview

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.

Latest papers

CardsList

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

  1. Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions

    Feb 23, 2026Luca Thiede, Abdulrahman Aldossary, Andreas Burger +9Quantum ChemistryComputational Quantum Chemistry

  2. MACE-POLAR-1: A Polarisable Electrostatic Foundation Model for Molecular Chemistry

    Feb 23, 2026Ilyes Batatia, William J. Baldwin, Domantas Kuryla +10Molecular Foundation ModelsMachine Learning Interatomic Potentials

  3. El Agente Quntur: A research collaborator agent for quantum chemistry

    Feb 4, 2026Juan B. Pérez-Sánchez, Yunheng Zou, Jorge A. Campos-Gonzalez-Angulo +12AI Agents for Scientific DiscoveryComputational Quantum Chemistry

  4. An ab initio foundation model of wavefunctions that accurately describes chemical bond breaking

    Jun 24, 2025Adam Foster, Zeno Schätzle, P. Bernát Szabó +7Quantum ChemistryQuantum Machine Learning