Physics-Aligned Electronic Ground-State Learning Improves Generalization
cs.LG, physics.chem-ph, quant-ph
Submitted: 2026-10-07
Updated: 2026-10-07
Code: https://github.com/ESEberhard/ML4DFT
Terminology
Sources
- Flow map matching with stochastic interpolants: A mathematical framework for consistency models
- DEQuify your force field: More efficient simulations using deep equilibrium models
- Shoot from the HIP: Hessian Interatomic Potentials without derivatives
- Transferable SCF-Acceleration through Solver-Aligned Initialization Learning
- Derivative Informed Learning of Exchange-Correlation Functionals
- Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
- Learning Equivariant Non-Local Electron Density Functionals
- QMugs: Quantum Mechanical Properties of Drug-like Molecules
- Learning from the electronic structure of molecules across the periodic table
- Machine Learning Hamiltonians are Accurate Energy-Force Predictors
- Adam: A Method for Stochastic Optimization
- Training a Foundation Model for Materials on a Budget
- Enhancing the Scalability and Applicability of Kohn-Sham Hamiltonians for Molecular Systems
- Muon is Scalable for LLM Training
- Towards A Transferable Acceleration Method for Density Functional Theory
- Accurate and scalable exchange-correlation with deep learning
- Efficient and Scalable Density Functional Theory Hamiltonian Prediction through Adaptive Sparsity
- Reducing SO(3) Convolutions to SO(2) for Efficient Equivariant GNNs
- Equivariant message passing for the prediction of tensorial properties and molecular spectra
- SE(3)-equivariant prediction of molecular wavefunctions and electronic densities
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