Unsupervised learning of representative local atomic arrangements in molecular dynamics data-Reference-Cited by-同舟云学术

Unsupervised learning of representative local atomic arrangements in molecular dynamics data

Published:2023 Issue:19 Volume:25 Page:13741-13754
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Roncoroni Fabrice¹^ORCID,Sanz-Matias Ana¹^ORCID,Sundararaman Siddharth¹^ORCID,Prendergast David¹^ORCID

Affiliation:

1. Joint Center for Energy Storage Research, The Molecular Foundry, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA

Abstract

We combine dimensionality reduction (UMAP) and unsupervised hierarchical clustering (HDBSCAN) to quantitatively characterize the coordination environment of chemical species within molecular dynamics data.

Funder

Basic Energy Sciences

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2023/CP/D3CP00525A

Reference64 articles.

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