4 papers
Accurate and scalable exchange-correlation with deep learning
Giulia Luise, Chin-Wei Huang, Thijs Vogels +25
Density Functional Theory (DFT) underpins much of modern computational chemistry and materials science. Yet, the reliability of DFT-derived predictions of experimentally measurable…
Enhancing Diffusion-Based Sampling with Molecular Collective Variables
Juno Nam, Bálint Máté, Artur P. Toshev +6
Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for mole…
Solvation Free Energies from Neural Thermodynamic Integration
Bálint Máté, François Fleuret, Tristan Bereau
We present a method for computing free-energy differences using thermodynamic integration with a neural network potential that interpolates between two target Hamiltonians. The int…
Neural Thermodynamic Integration: Free Energies from Energy-based Diffusion Models
Bálint Máté, François Fleuret, Tristan Bereau
Thermodynamic integration (TI) offers a rigorous method for estimating free-energy differences by integrating over a sequence of interpolating conformational ensembles. However, TI…