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  How robust are modern graph neural network potentials in long and hot molecular dynamics simulations?

Stocker, S., Gasteiger, J., Becker, F., Günnemann, S., & Margraf, J. (2022). How robust are modern graph neural network potentials in long and hot molecular dynamics simulations? Machine Learning: Science and Technology, 3(4): 045010. doi:10.26434/chemrxiv-2022-mc4gb.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-3120-A Version Permalink: https://hdl.handle.net/21.11116/0000-000C-3F6E-4
Genre: Journal Article

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 Creators:
Stocker, Sina1, Author           
Gasteiger, Johannes, Author
Becker, Florian, Author
Günnemann, Stephan, Author
Margraf, Johannes1, Author           
Affiliations:
1Theory, Fritz Haber Institute, Max Planck Society, ou_634547              

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Language(s): eng - English
 Dates: 2022-04-012022-07-222022-04-262022-10-112022-11-01
 Publication Status: Published online
 Pages: 8
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.26434/chemrxiv-2022-mc4gb
DOI: 10.1088/2632-2153/ac9955
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Title: Machine Learning: Science and Technology
  Abbreviation : Mach. Learn.: Sci. Technol.
Source Genre: Journal
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Publ. Info: Bristol, UK : IOP Publishing
Pages: 8 Volume / Issue: 3 (4) Sequence Number: 045010 Start / End Page: - Identifier: ISSN: 2632-2153
CoNE: https://pure.mpg.de/cone/journals/resource/2632-2153