Work

Guest Editorial: Special Topic on Data-Enabled Theoretical Chemistry

Year: 2018

Type: editorial

Abstract: A survey of the contributions to the Special Topic on Data-enabled Theoretical Chemistry is given, including a glossary of relevant machine learning terms.

Source: The Journal of Chemical Physics

DOI: 10.1063/1.5043213

Authors: Matthias Rupp, O. Anatole von Lilienfeld, Kieron Burke

Institutions: Fritz Haber Institute of the Max Planck Society, University of Basel, University of California, Irvine

Language: English

Topic: Machine Learning in Materials Science

Subfield: Materials Chemistry

Field: Materials Science

Domain: Physical Sciences

Open Access status: green

Funders: National Science Foundation, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Horizon 2020 Framework Programme, National Center of Competence in Research Materials’ Revolution: Computational Design and Discovery of Novel Materials

Awards: Multiscale dynamics of dog rabies elimination, From atomistic exploration of chemical compound space towards bio-molecular design: Quantum mechanical rational compound design (QM-RCD), Systematic approach to Density Functional Theory, G7137499191

OpenAlex ID: W2807616984

Cites: 80

Cited by: 104

Related to: 10