Neural network atomistic potentials for global energy minima search in carbon clusters-Reference-Cited by-同舟云学术

Neural network atomistic potentials for global energy minima search in carbon clusters

Published:2023 Issue:32 Volume:25 Page:21173-21182
ISSN:1463-9076
Container-title:Physical Chemistry Chemical Physics
language:en
Short-container-title:Phys. Chem. Chem. Phys.

Author:

Tkachenko Nikolay V.¹^ORCID,Tkachenko Anastasiia A.²,Nebgen Benjamin³^ORCID,Tretiak Sergei³^ORCID,Boldyrev Alexander I.¹^ORCID

Affiliation:

1. Department of Chemistry and Biochemistry, Utah State University, Logan, Utah 84322-0300, USA

2. Department of Computer Science, Utah State University, Logan, Utah 84322-0300, USA

3. Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA

Abstract

This article explores the application of NN atomistic potentials, specifically ANI-1ccx and ANI-nr, for efficient global minima optimization in carbon clusters. Combined with the cluster distance restriction, they are robust PES pre-samplers.

Funder

Los Alamos National Laboratory

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/D3CP02317F

Reference62 articles.

1. Single Atoms and Clusters Based Nanomaterials for Hydrogen Evolution, Oxygen Evolution Reactions, and Full Water Splitting

2. High-Density Ultra-small Clusters and Single-Atom Fe Sites Embedded in Graphitic Carbon Nitride (g-C3N4) for Highly Efficient Catalytic Advanced Oxidation Processes

3. Highly active subnanometer Rh clusters derived from Rh-doped SrTiO3 for CO2 reduction

4. Lattice-confined Ru clusters with high CO tolerance and activity for the hydrogen oxidation reaction

5. Tin Metal Cluster Compounds as New Third-Order Nonlinear Optical Materials by Computational Study

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Bonobo Optimizer: A New Tool Toward the Global Optimization of Small Atomic Clusters;The Journal of Physical Chemistry A;2024-05-02

2. Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices;The Journal of Physical Chemistry C;2024-03-20

3. Transferable machine learning interatomic potential for carbon hydrogen systems;Physical Chemistry Chemical Physics;2024