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

Rauer, Clemens; Bereau, Tristan

doi:10.1063/5.0012230

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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: https://hdl.handle.net/21.11116/0000-0006-B6DB-6 Version Permalink: https://hdl.handle.net/21.11116/0000-0006-B6DC-5

Genre: Journal Article

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Creators:
Rauer, Clemens¹, Author
Bereau, Tristan^{1, 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: Published Online: 2020-07-01Date issued: 2020-07-07

Publication Status: Issued

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Identifiers: DOI: 10.1063/5.0012230

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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