Towards Exact Molecular Dynamics Simulations with Machine-Learned Force Fields

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials are based on mechanistic models of interatomic interactions, which often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in a data-driven way, thus greatly reducing the intrinsic complexity of the force field learning problem. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy [coupled cluster with single, double, and perturbative triple excitations] and for the first time allows converged molecular dynamics simulations at the level of fully quantized electrons and nuclei for flexible molecules with up to a few dozen atoms.