Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces-Reference-Cited by-同舟云学术

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

Published:2021 Issue:38 Volume:23 Page:22022-22034
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Lamoureux Philomena Schlexer¹²^ORCID,Choksi Tej S.¹²^ORCID,Streibel Verena¹²^ORCID,Abild-Pedersen Frank²^ORCID

Affiliation:

1. Department of Chemical Engineering, Stanford University, 443 Via Ortega, Stanford, CA 94305, USA

2. SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis, 2575 Sand Hill Road, Menlo Park, California 94025, USA

Abstract

We use artificial intelligence methods to instantaneously predict the stability of atomic sites in nanoparticles having a wide range of sizes, shapes, and compositions.

Funder

Alexander von Humboldt-Stiftung

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://pubs.rsc.org/en/content/articlepdf/2021/CP/D1CP02198B

Reference86 articles.

1. Universality in Heterogeneous Catalysis

2. Organic chemistry on solid surfaces