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Title: Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites
Authors: How, Wei Bin
Wang, Bipeng
Chu, Weibin
Kovalenko, Sergiy M.
Tkatchenko, Alexandre
Prezhdo, Oleg V.
Keywords: Science::Chemistry
Issue Date: 2022
Source: How, W. B., Wang, B., Chu, W., Kovalenko, S. M., Tkatchenko, A. & Prezhdo, O. V. (2022). Dimensionality reduction in machine learning for nonadiabatic molecular dynamics: Effectiveness of elemental sublattices in lead halide perovskites. Journal of Chemical Physics, 156(5), 054110-.
Journal: Journal of Chemical Physics
Abstract: Supervised machine learning (ML) and unsupervised ML have been performed on descriptors generated from nonadiabatic (NA) molecular dynamics (MD) trajectories representing non-radiative charge recombination in CsPbI3, a promising solar cell and optoelectronic material. Descriptors generated from every third atom of the iodine sublattice alone are sufficient for a satisfactory prediction of the bandgap and NA coupling for the use in the NA-MD simulation of nonradiative charge recombination, which has a strong influence on material performance. Surprisingly, descriptors based on the cesium sublattice perform better than those of the lead sublattice, even though Cs does not contribute to the relevant wavefunctions, while Pb forms the conduction band and contributes to the valence band. Simplification of the ML models of the NA-MD Hamiltonian achieved by the present analysis helps to overcome the high computational cost of NA-MD through ML and increase the applicability of NA-MD simulations.
ISSN: 0021-9606
DOI: 10.1063/5.0078473
Schools: School of Physical and Mathematical Sciences 
Rights: © 2022 Author(s). All rights reserved. This paper was published by AIP Publishing in Journal of Chemical Physics and is made available with permission of Author(s).
Fulltext Permission: open
Fulltext Availability: With Fulltext
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