Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding-Reference-Cited by-同舟云学术

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Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding

Published:2023-06-08 Issue:12 Volume:19 Page:3567-3579
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Ko Tsz Wai¹^ORCID,Finkler Jonas A.²,Goedecker Stefan²,Behler Jörg¹³⁴^ORCID

Affiliation:

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

2. Department of Physics, Universität Basel, Klingelbergstrasse 82, 4056 Basel, Switzerland

3. Lehrstuhl für Theoretische Chemie II, Ruhr-Universität Bochum, 44780 Bochum, Germany

4. Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, 44780 Bochum, Germany

Funder

Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung

Deutsche Forschungsgemeinschaft

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.2c01146

Reference84 articles.

1. Perspective: Machine learning potentials for atomistic simulations

2. Machine Learning Interatomic Potentials as Emerging Tools for Materials Science

3. Machine learning potentials for extended systems: a perspective

4. Machine Learning for Molecular Simulation

5. Neural Network Potentials: A Concise Overview of Methods

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