Controlled exploration of chemical space by machine learning of coarse-grained 
representations

Hoffmann, Christian; Menichetti, Roberto; Kanekal, Kiran; Bereau, Tristan

doi:10.1103/PhysRevE.100.033302

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Controlled exploration of chemical space by machine learning of coarse-grained representations

Hoffmann, C., Menichetti, R., Kanekal, K., & Bereau, T. (2019). Controlled exploration of chemical space by machine learning of coarse-grained representations. Physical Review E, 100(3): 033302. doi:10.1103/PhysRevE.100.033302.

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Item Permalink: https://hdl.handle.net/21.11116/0000-0004-AF17-E Version Permalink: https://hdl.handle.net/21.11116/0000-0005-D9FF-8

Genre: Journal Article

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Creators:
Hoffmann, Christian¹, Author
Menichetti, Roberto¹, Author
Kanekal, Kiran¹, Author
Bereau, Tristan^{1, 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: Published Online: 2019Date issued: 2019

Publication Status: Issued

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Identifiers: DOI: 10.1103/PhysRevE.100.033302

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Title: Physical Review E

Other : Phys. Rev. E

Source Genre: Journal

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Publ. Info: Melville, NY : American Physical Society

Pages: - Volume / Issue: 100 (3) Sequence Number: 033302 Start / End Page: - Identifier: ISSN: 1539-3755
CoNE: https://pure.mpg.de/cone/journals/resource/954925225012