Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations-Reference-Cited by-同舟云学术

Incorporating Electronic Information into Machine Learning Potential Energy Surfaces via Approaching the Ground-State Electronic Energy as a Function of Atom-Based Electronic Populations

Published:2020-06-05 Issue:7 Volume:16 Page:4256-4270
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Xie Xiaowei¹²^ORCID,Persson Kristin A.²³^ORCID,Small David W.¹⁴^ORCID

Affiliation:

1. Department of Chemistry, University of California, Berkeley, California 94720, United States

2. Energy Technologies Area, Lawrence Berkeley National Laboratory, Berkeley, California 94720, United States

3. Department of Materials Science and Engineering, University of California, Berkeley, California 94720, United States

4. Molecular Graphics and Computation Facility, College of Chemistry, University of California, Berkeley 94720, California United States

Funder

NIH Office of the Director

Basic Energy Sciences

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.0c00217

Reference135 articles.

1. Parr, R. G. Horizons of Quantum Chemistry; Springer, 1980; pp 5–15.

2. Unified Approach for Molecular Dynamics and Density-Functional Theory

3. Ab Initio Molecular Dynamics

4. Molecular dynamics simulations

5. Molecular dynamics simulations of biomolecules

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