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
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