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  Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning

Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012). Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning. Physical Review Letters, 108(5): 058301. doi:10.1103/PhysRevLett.108.058301.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-C6EF-A Version Permalink: https://hdl.handle.net/11858/00-001M-0000-000F-C6F1-2
Genre: Journal Article

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2012
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 Creators:
Rupp, Matthias1, 2, Author
Tkatchenko, Alexandre2, 3, Author           
Müller, Klaus-Robert1, 2, Author
von Lilienfeld, O. Anatole2, 4, Author
Affiliations:
1Machine Learning Group, Technical University of Berlin, Franklinstr 28/29, 10587 Berlin, Germany, ou_persistent22              
2Institute of Pure and Applied Mathematics, University of California Los Angeles, Los Angeles,California 90095, USA, ou_persistent22              
3Theory, Fritz Haber Institute, Max Planck Society, ou_634547              
4Argonne Leadership Computing Facility, Argonne National Laboratory, Argonne, Illinois 60439, USA, ou_persistent22              

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Language(s): eng - English
 Dates: 2012-01-31
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1103/PhysRevLett.108.058301
 Degree: -

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Title: Physical Review Letters
  Other : Phys. Rev. Lett.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y., etc. : American Physical Society.
Pages: 5 Volume / Issue: 108 (5) Sequence Number: 058301 Start / End Page: - Identifier: ISSN: 0031-9007
CoNE: https://pure.mpg.de/cone/journals/resource/954925433406