Efficient interatomic descriptors for accurate machine learning force fields of extended molecules.

ABSTRACT: Machine learning force fields (MLFFs) are gradually evolving towards enabling molecular dynamics simulations of molecules and materials with ab initio accuracy but at a small fraction of the computational cost. However, several challenges remain to be addressed to enable predictive MLFF simulations of realistic molecules, including: (1) developing efficient descriptors for non-local interatomic interactions, which are essential to capture long-range molecular fluctuations, and (2) reducing the dimensionality of the descriptors to enhance the applicability and interpretability of MLFFs. Here we propose an automatized approach to substantially reduce the number of interatomic descriptor features while preserving the accuracy and increasing the efficiency of MLFFs. To simultaneously address t

SUBMITTER: Kabylda A 

PROVIDER: S-EPMC10272221 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature