Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence-Reference-Cited by-同舟云学术

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Reconstructing Kernel-Based Machine Learning Force Fields with Superlinear Convergence

Published:2023-05-08 Issue:14 Volume:19 Page:4619-4630
ISSN:1549-9618
Container-title:Journal of Chemical Theory and Computation
language:en
Short-container-title:J. Chem. Theory Comput.

Author:

Blücher Stefan¹²^ORCID,Müller Klaus-Robert¹²³⁴⁵^ORCID,Chmiela Stefan¹²^ORCID

Affiliation:

1. BIFOLD−Berlin Institue for the Foundations of Learning and Data, 10587 Berlin, Germany

2. Technische Universität Berlin, Machine Learning Group, 10587 Berlin, Germany

3. Department of Artificial Intelligence, Korea University, Seoul 136-713, Korea

4. Max Planck Institute for Informatics, 66123 Saarbrücken, Germany

5. Google Research, Brain Team, 10117 Berlin, Germany

Funder

Bundesministerium f?r Bildung und Forschung

Institute for Information and Communications Technology Promotion

Publisher

American Chemical Society (ACS)

Subject

Physical and Theoretical Chemistry,Computer Science Applications

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jctc.2c01304

Reference79 articles.

1. Machine Learning for Molecular Simulation

2. Machine Learning Force Fields

3. Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems

4. Choosing the right molecular machine learning potential

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

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Euclidean transformer for fast and stable machine learned force fields;Nature Communications;2024-08-06

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