Exploring Potential Energy Surfaces Using Reinforcement Machine Learning-Reference-Cited by-同舟云学术

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Exploring Potential Energy Surfaces Using Reinforcement Machine Learning

Published:2022-06-16 Issue:13 Volume:62 Page:3169-3179
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Mills Alexis W.¹,Goings Joshua J.¹^ORCID,Beck David²,Yang Chao³,Li Xiaosong¹^ORCID

Affiliation:

1. Department of Chemistry, University of Washington, Seattle, Washington 98195, United States

2. Department of Chemical Engineering, University of Washington, Seattle, Washington 98195, United States

3. Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States

Funder

Basic Energy Sciences

Publisher

American Chemical Society (ACS)

Subject

Library and Information Sciences,Computer Science Applications,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jcim.2c00373

Reference39 articles.

1. Optimization of equilibrium geometries and transition structures

2. New algorithm for the optimization of geometries in local density functional theory

3. Search for stationary points on surfaces

4. Geometry optimization by direct inversion in the iterative subspace

5. Ab initio geometry optimization for large molecules

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