The principal component analysis of the ring deformation in the nonadiabatic surface hopping dynamics

The all-atomic full-dimensional-level simulations of nonadiabatic molecular dynamics (NAMD) in large realistic systems has received high research interest in recent years. However, such NAMD simulations normally generate an enormous amount of time-dependent high-dimensional data, leading to a significant challenge in result analyses. Based on unsupervised machine learning (ML) methods, considerable efforts were devoted to developing novel and easy-to-use analysis tools for the identification of photoinduced reaction channels and the comprehensive understanding of complicated molecular motions in NAMD simulations. Here, we tried to survey recent advances in this field, particularly to focus on how to use unsupervised ML methods to analyze the trajectory-based NAMD simulation results. Our purpose is to offer a comprehensive discussion on several essential components of this analysis protocol, including the selection of ML methods, the construction of molecular descriptors, the establishment of analytical frameworks, their advantages and limitations, and persistent challenges.

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Nonadiabatic molecular dynamics simulations shed light on the timescale of furylfulgide photocyclisation

Kochman

2024

New J. Chem.

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The principal component analysis of the ring deformation in the nonadiabatic surface hopping dynamics

Abstract: The analysis of the leading active molecular motions in the on-the-fly trajectory surface hopping simulation provides the essential information to understand the geometric evolution in nonadiabatic dynamics. When the ring...

Cited by 3 publications

References 103 publications

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Unsupervised Machine Learning in the Analysis of Nonadiabatic Molecular Dynamics Simulation

Nonadiabatic molecular dynamics simulations shed light on the timescale of furylfulgide photocyclisation

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