Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields-Reference-Cited by-同舟云学术

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Algorithmic Differentiation for Automated Modeling of Machine Learned Force Fields

Published:2022-10-24 Issue:43 Volume:13 Page:10183-10189
ISSN:1948-7185
Container-title:The Journal of Physical Chemistry Letters
language:en
Short-container-title:J. Phys. Chem. Lett.

Author:

Schmitz Niklas Frederik¹,Müller Klaus-Robert¹²³⁴⁵^ORCID,Chmiela Stefan¹²^ORCID

Affiliation:

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

2. BIFOLD - Berlin Institute for the Foundations of Learning and Data, 10587Berlin, Germany

3. Department of Artificial Intelligence, Korea University, Seongbuk-gu, Seoul02841, Korea

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

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

Funder

Bundesministerium f?r Bildung und Forschung

Information & Communications Technology Planning & Evaluation

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.2c02632

Reference64 articles.

1. Searching for exotic particles in high-energy physics with deep learning

2. Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

3. Machine Learning Force Fields

4. Exploring chemical compound space with quantum-based machine learning

5. Machine Learning for Molecular Simulation

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3. Stable and accurate atomistic simulations of flexible molecules using conformationally generalisable machine learned potentials;Chemical Science;2024

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