A machine learning model trained on DFT data that predicts atomic energies, forces, and stresses from atomic positions and species. Examples include CHGNet, M3GNet, MACE, and SchNet. Enables molecular dynamics and structure relaxation at near-DFT accuracy but orders of magnitude faster. CHGNet uniquely incorporates magnetic moments and charge states.