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

The Journal of Physical Chemistry Letters. 2024. Vol. 15. No. 41. P. 10384–10391.

Wang B., Liu D., Wu Y., Andrey S. Vasenko, Prezhdo O.

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 grain boundary sliding and a spontaneous fluctuation half a nanosecond later, both events giving rise to persistent deep electronic trap states that impact charge carrier lifetime and transport and material performance. The approach offers a generalizable and simple method for identifying outlier events in complex condensed matter, molecular, and nanoscale systems.

Keywords: машинное обучение, machine learning, перовскиты, Perovskites, границы зерен, grain boundary, редкие события, Rare Events, Quantum Molecular Dynamics, outlier detection indices, квантовая молекулярная динамика, обнаружение выбросов

Publication based on the results of: Nanoelectronics of low-dimensional systems (2024)