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  Hydration free energies from kernel-based machine learning: Compound-database bias

Rauer, C., & Bereau, T. (2020). Hydration free energies from kernel-based machine learning: Compound-database bias. The Journal of Chemical Physics, 153(1): 014101. doi:10.1063/5.0012230.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0006-B6DB-6 Version Permalink: http://hdl.handle.net/21.11116/0000-0006-B6DC-5
Genre: Journal Article

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 Creators:
Rauer, Clemens1, Author              
Bereau, Tristan1, 2, 3, Author              
Affiliations:
1Dept. Kremer: Polymer Theory, MPI for Polymer Research, Max Planck Society, ou_1800287              
2Van 'T Hoff Institute for Molecular Sciences and Informatics Institute, University of Amsterdam, Amsterdam, Netherlands, ou_persistent22              
3Emmy Noether Group Bereau: Biomolecular Simulations, MPI for Polymer Research, Max Planck Society, ou_2344697              

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Language(s): eng - English
 Dates: 2020-07-012020-07-07
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1063/5.0012230
 Degree: -

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Title: The Journal of Chemical Physics
  Other : J. Chem. Phys.
Source Genre: Journal
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Publ. Info: Woodbury, N.Y. : American Institute of Physics
Pages: - Volume / Issue: 153 (1) Sequence Number: 014101 Start / End Page: - Identifier: ISSN: 0021-9606
CoNE: https://pure.mpg.de/cone/journals/resource/954922836226