Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials-Reference-Cited by-同舟云学术

Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials

Author:

Korolev Vadim V.¹²^ORCID,Mitrofanov Artem¹²^ORCID,Marchenko Ekaterina I.³⁴,Eremin Nickolay N.⁴,Tkachenko Valery¹,Kalmykov Stepan N.²

Affiliation:

1. Science Data Software, LLC, 14909 Forest Landing Circle, Rockville, Maryland 20850, United States

2. Department of Chemistry, Lomonosov Moscow State University, Moscow 119991, Russia

3. Laboratory of New Materials for Solar Energetics, Department of Materials Science, Lomonosov Moscow State University, Moscow 119991, Russia

4. Department of Geology, Lomonosov Moscow State University, Moscow 119991, Russia

Publisher

American Chemical Society (ACS)

Subject

Materials Chemistry,General Chemical Engineering,General Chemistry

Link

Reference95 articles.

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