Learning to Optimize Molecular Geometries Using Reinforcement Learning-Reference-Cited by-同舟云学术

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Learning to Optimize Molecular Geometries Using Reinforcement Learning

Published:2021-01-20 Issue:2 Volume:17 Page:818-825
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Ahuja Kabir¹,Green William H.²^ORCID,Li Yi-Pei³^ORCID

Affiliation:

1. Udaan, Bengaluru, Karnataka 560001, India

2. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, United States

3. Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan

Funder

Defense Advanced Research Projects Agency

Ministry of Science and Technology, Taiwan

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.0c00971

Reference47 articles.

1. Hratchian, H. P.; Schlegel, H. B. Theory and Applications of Computational Chemistry; Elsevier, 2005; pp 195–249.

2. Exploring potential energy surfaces for chemical reactions: An overview of some practical methods

3. Schlegel, H. B. Advanced Chemical Physics; John Wiley & Sons, 2007; pp 249–286.

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