Transferable Atomic Multipole Machine Learning Models for Small Organic 
Molecules

Bereau, Tristan; Andrienko, Denis; von Lilienfeld, O. A.

doi:10.1021/acs.jctc.5b00301

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Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules

Bereau, T., Andrienko, D., & von Lilienfeld, O. A. (2015). Transferable Atomic Multipole Machine Learning Models for Small Organic Molecules. Journal of Chemical Theory and Computation, 11(7), 3225-3233. doi:10.1021/acs.jctc.5b00301.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0028-3325-0 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0028-3326-E

Genre: Journal Article

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Creators:
Bereau, Tristan¹, Author
Andrienko, Denis¹, Author
von Lilienfeld, O. A., Author

Affiliations:
1Dept. Kremer: Polymer Theory, MPI for Polymer Research, Max Planck Society, ou_1800287

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Language(s): eng - English

Dates: Date issued: 2015

Publication Status: Issued

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Identifiers: DOI: 10.1021/acs.jctc.5b00301

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Title: Journal of Chemical Theory and Computation

Other : J. Chem. Theory Comput.

Source Genre: Journal

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Publ. Info: Washington, D.C. : American Chemical Society

Pages: - Volume / Issue: 11 (7) Sequence Number: - Start / End Page: 3225 - 3233 Identifier: Other: 1549-9618
CoNE: https://pure.mpg.de/cone/journals/resource/111088195283832