Generative adversarial networks for transition state geometry prediction-Reference-Cited by-同舟云学术

Generative adversarial networks for transition state geometry prediction

Published:2021-07-14 Issue:2 Volume:155 Page:024116
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Makoś Małgorzata Z.¹^ORCID,Verma Niraj¹^ORCID,Larson Eric C.²,Freindorf Marek¹^ORCID,Kraka Elfi¹^ORCID

Affiliation:

1. Computational and Theoretical Chemistry Group (CATCO), Department of Chemistry, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA

2. Computer Science Department, Southern Methodist University, 3215 Daniel Avenue, Dallas, Texas 75275-0314, USA

Funder

National Science Foundation

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

https://aip.scitation.org/doi/am-pdf/10.1063/5.0055094

Reference73 articles.

1. Generative Adversarial Networks (GANs)

2. The Advent of Generative Chemistry

3. Variational transition state theory: theoretical framework and recent developments

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

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