Machine learning molecular dynamics simulations of liquid methanol

As the simplest hydrogen-bonded alcohol,liquid methanol has attracted intensive experimental and theoretical interest.However,theoretical investigations on this system have primarily relied on empirical intermolecular force fields or ab initio molecular dynamics with semilocal density functionals.Inspired by recent studies on bulk water using increasingly accurate machine learning force fields,we report a new machine learning force field for liquid methanol with a hybrid functional revPBE0 plus dispersion correction.Molecular dynamics simulations on this machine learning force field are orders of magnitude faster than ab initio molecular dynamics simulations,yielding the radial distribution functions,selfdiffusion coefficients,and hydrogen bond network properties with very small statistical errors.The resulting structural and dynamical properties are compared well with the experimental data,demonstrating the superior accuracy of this machine learning force field.This work represents a successful step toward a first-principles description of this benchmark system and showcases the general applicability of the machine learning force field in studying liquid systems.

Efficient Selection of Linearly Independent Atomic Features for Accurate Machine Learning Potentials

Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.