Significance of the Chemical Environment of an Element in Nonadiabatic Molecular Dynamics: Feature Selection and Dimensionality Reduction with Machine Learning.


Journal

The journal of physical chemistry letters
ISSN: 1948-7185
Titre abrégé: J Phys Chem Lett
Pays: United States
ID NLM: 101526034

Informations de publication

Date de publication:
23 Dec 2021
Historique:
pubmed: 14 12 2021
medline: 8 2 2022
entrez: 13 12 2021
Statut: ppublish

Résumé

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 CsPbI

Identifiants

pubmed: 34902248
doi: 10.1021/acs.jpclett.1c03469
doi:

Substances chimiques

Calcium Compounds 0
Oxides 0
perovskite 12194-71-7
Titanium D1JT611TNE

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

12026-12032

Auteurs

Wei Bin How (WB)

Division of Chemistry and Biological Chemistry, School of Physical and Mathematical Sciences, Nanyang Technological University, 637371 Singapore.

Bipeng Wang (B)

Department of Chemical Engineering, University of Southern California, Los Angeles, California 90089, United States.

Weibin Chu (W)

Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.

Alexandre Tkatchenko (A)

Department of Physics and Materials Science, University of Luxembourg, L-1511 Luxembourg City, Luxembourg.

Oleg V Prezhdo (OV)

Department of Chemical Engineering, University of Southern California, Los Angeles, California 90089, United States.
Department of Chemistry, University of Southern California, Los Angeles, California 90089, United States.
Department of Physics and Astronomy, University of Southern California, Los Angeles, California 90089, United States.

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Classifications MeSH