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  Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids

Scherer, C., Scheid, R., Andrienko, D., & Bereau, T. (2020). Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids. Journal of Chemical Theory and Computation, 16(5), 3194-3204. doi:10.1021/acs.jctc.9b01256.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-6F12-A Version Permalink: http://hdl.handle.net/21.11116/0000-0006-7389-E
Genre: Journal Article

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acs.jctc.9b01256.pdf (Publisher version), 2MB
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Copyright Date:
2020
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American Chemical Society

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 Creators:
Scherer, Christoph1, Author              
Scheid, René1, Author              
Andrienko, Denis1, Author              
Bereau, Tristan1, 2, Author              
Affiliations:
1Dept. Kremer: Polymer Theory, MPI for Polymer Research, Max Planck Society, ou_1800287              
2Emmy Noether Group Bereau: Biomolecular Simulations, MPI for Polymer Research, Max Planck Society, ou_2344697              

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Language(s): eng - English
 Dates: 2020-04-132020
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1021/acs.jctc.9b01256
 Degree: -

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Title: Journal of Chemical Theory and Computation
  Other : J. Chem. Theory Comput.
Source Genre: Journal
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Affiliations:
Publ. Info: Washington, D.C. : American Chemical Society
Pages: - Volume / Issue: 16 (5) Sequence Number: - Start / End Page: 3194 - 3204 Identifier: ISSN: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832