Parallel Multistream Training of High-Dimensional Neural Network Potentials-Reference-Cited by-同舟云学术

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Parallel Multistream Training of High-Dimensional Neural Network Potentials

Published:2019-04-17 Issue:5 Volume:15 Page:3075-3092
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Singraber Andreas¹^ORCID,Morawietz Tobias²^ORCID,Behler Jörg³^ORCID,Dellago Christoph¹^ORCID

Affiliation:

1. Faculty of Physics, University of Vienna, Boltzmanngasse 5, Vienna, Austria

2. Department of Chemistry, Stanford University, Stanford, California 94305, United States

3. Universität Göttingen, Institut für Physikalische Chemie, Theoretische Chemie, Tammannstraße 6, 37077 Göttingen, Germany

Funder

Bundesministerium f?r Wissenschaft, Forschung und Wirtschaft

Deutsche Forschungsgemeinschaft

Austrian Science Fund

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.8b01092

Reference109 articles.

1. Machine Learning for Science: State of the Art and Future Prospects

2. Machine learning: Trends, perspectives, and prospects

3. Machine learning for molecular and materials science

4. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

5. First Principles Neural Network Potentials for Reactive Simulations of Large Molecular and Condensed Systems

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