3 papers
physics.comp-ph2025
Learning Smooth and Expressive Interatomic Potentials for Physical Property Prediction
Xiang Fu, Brandon M. Wood, Luis Barroso-Luque +4
Machine learning interatomic potentials (MLIPs) have become increasingly effective at approximating quantum mechanical calculations at a fraction of the computational cost. However…
physics.chem-ph2024
Nutmeg and SPICE: Models and Data for Biomolecular Machine Learning
Peter Eastman, Benjamin P. Pritchard, John D. Chodera +1
We describe version 2 of the SPICE dataset, a collection of quantum chemistry calculations for training machine learning potentials. It expands on the original dataset by adding mu…
physics.chem-ph2024
Enhancing Protein-Ligand Binding Affinity Predictions using Neural Network Potentials
Francesc Sabanes Zariquiey, Raimondas Galvelis, Emilio Gallicchio +3
This letter gives results on improving protein-ligand binding affinity predictions based on molecular dynamics simulations using machine learning potentials with a hybrid neural ne…