Breaking the size limitation of nonadiabatic molecular dynamics in condensed matter systems with local descriptor machine learning.

ABSTRACT: Nonadiabatic molecular dynamics (NA-MD) is a powerful tool to model far-from-equilibrium processes, such as photochemical reactions and charge transport. NA-MD application to condensed phase has drawn tremendous attention recently for development of next-generation energy and optoelectronic materials. Studies of condensed matter allow one to employ efficient computational tools, such as density functional theory (DFT) and classical path approximation (CPA). Still, system size and simulation timescale are strongly limited by costly ab initio calculations of electronic energies, forces, and NA couplings. We resolve the limitations by developing a fully machine learning (ML) approach in which all the above properties are obtained using neural networks based on local descriptors. The ML models

SUBMITTER: Liu D 

PROVIDER: S-EPMC11388379 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature