Machine learning potentials for extended systems: a perspective
Machine learning potentials for extended systems: a perspective
About this item
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Author / Creator
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title
Language
English
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Publication information
Publisher
Berlin/Heidelberg: Springer Berlin Heidelberg
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Scope and Contents
Contents
In the past two and a half decades machine learning potentials have evolved from a special purpose solution to a broadly applicable tool for large-scale atomistic simulations. By combining the efficiency of empirical potentials and force fields with an accuracy close to first-principles calculations they now enable computer simulations of a wide ra...
Alternative Titles
Full title
Machine learning potentials for extended systems: a perspective
Authors, Artists and Contributors
Author / Creator
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Record Identifier
TN_cdi_proquest_journals_2553315978
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2553315978
Other Identifiers
ISSN
1434-6028
E-ISSN
1434-6036
DOI
10.1140/epjb/s10051-021-00156-1
