Identifying Rare Events in Quantum Molecular Dynamics of Nanomaterials with Outlier Detection Indices.

ABSTRACT: Nanoscale and condensed matter systems evolve on multiple length- and time-scales, and rare events such as local phase transformation, ion segregation, defect migration, interface reconstruction, and grain boundary sliding can have a profound influence on material properties. We demonstrate how outlier detection indices can be used to identify rare events in machine-learning based, high-dimensional molecular dynamics (MD) simulations. Designed to order data-points from typical to untypical, the indices enable one to capture atomic events that are hard to detect otherwise. We demonstrate the approach with a nanosecond MD simulation of a grain boundary in a metal halide perovskite that is extensively studied for solar energy and optoelectronic applications. The method captures the initial gr

SUBMITTER: Wang B 

PROVIDER: S-EPMC11492377 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature