Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163142
Title: Significance of the chemical environment of an element in nonadiabatic molecular dynamics: feature selection and dimensionality reduction with machine learning
Authors: How, Wei Bin
Wang, Bipeng
Chu, Weibin
Tkatchenko, Alexandre
Prezhdo, Oleg V.
Keywords: Science::Chemistry
Issue Date: 2021
Source: How, W. B., Wang, B., Chu, W., Tkatchenko, A. & Prezhdo, O. V. (2021). Significance of the chemical environment of an element in nonadiabatic molecular dynamics: feature selection and dimensionality reduction with machine learning. Journal of Physical Chemistry Letters, 12(50), 12026-12032. https://dx.doi.org/10.1021/acs.jpclett.1c03469
Journal: Journal of Physical Chemistry Letters 
Abstract: Using supervised and unsupervised machine learning (ML) on features generated from nonadiabatic (NA) molecular dynamics (MD) trajectories under the classical path approximation, we demonstrate that mutual information with the NA Hamiltonian can be used for feature selection and model simplification. Focusing on CsPbI3, a popular metal halide perovskite, we observe that the chemical environment of a single element is sufficient for predicting the NA Hamiltonian. The conclusion applies even to Cs, although Cs does not contribute to the relevant wave functions. Interatomic distances between Cs and I or Pb and the octahedral tilt angle are the most important features. We reduce a typical 360-parameter ML force-field model to just a 12-parameter NA Hamiltonian model, while maintaining a high NA-MD simulation quality. Because NA-MD is a valuable tool for studying excited state processes, overcoming its high computational cost through simple ML models will streamline NA-MD simulations and expand the ranges of accessible system size and simulation time.
URI: https://hdl.handle.net/10356/163142
ISSN: 1948-7185
DOI: 10.1021/acs.jpclett.1c03469
Schools: School of Physical and Mathematical Sciences 
Rights: © 2021 American Chemical Society. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SPMS Journal Articles

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