TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials-Reference-Cited by-同舟云学术

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TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation of the ANI Neural Network Potentials

Published:2020-06-22 Issue:7 Volume:60 Page:3408-3415
ISSN:1549-9596
Container-title:Journal of Chemical Information and Modeling
language:en
Short-container-title:J. Chem. Inf. Model.

Author:

Gao Xiang¹,Ramezanghorbani Farhad¹^ORCID,Isayev Olexandr²^ORCID,Smith Justin S.³,Roitberg Adrian E.¹^ORCID

Affiliation:

1. Department of Chemistry, University of Florida, Gainesville, Florida 32611, United States

2. Department of Chemistry, Carnegie Mellon University, Pittsburgh Pennsylvania 15213, United States

3. Center for Nonlinear Studies and Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545, United States

Funder

National Science Foundation

U.S. Department of Energy

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.0c00451

Reference48 articles.

1. Why quantum chemistry is hard

2. Quantum Computational Complexity

3. Self-Consistent Equations Including Exchange and Correlation Effects

4. Coupled-cluster theory in quantum chemistry

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