AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials-Reference-Cited by-同舟云学术

AP-Net: An atomic-pairwise neural network for smooth and transferable interaction potentials

Published:2020-07-28 Issue:4 Volume:153 Page:044112
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Glick Zachary L.¹^ORCID,Metcalf Derek P.¹^ORCID,Koutsoukas Alexios²,Spronk Steven A.²^ORCID,Cheney Daniel L.²,Sherrill C. David¹^ORCID

Affiliation:

1. Center for Computational Molecular Science and Technology, School of Chemistry and Biochemistry, and School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332-0400, USA

2. Molecular Structure and Design, Bristol Myers Squibb Company, P.O. Box 5400, Princeton, New Jersey 08543, USA

Funder

National Science Foundation

U.S. Department of Energy

Bristol-Myers Squibb

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0011521

Reference58 articles.

1. Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

2. Deep Learning in Chemistry

3. Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

4. A Machine Learning Approach for Prediction of Rate Constants

5. Chemical shifts in molecular solids by machine learning

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