4.6 Review

Advances of machine learning in molecular modeling and simulation

Journal

CURRENT OPINION IN CHEMICAL ENGINEERING
Volume 23, Issue -, Pages 51-57

Publisher

ELSEVIER SCI LTD
DOI: 10.1016/j.coche.2019.02.009

Keywords

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Funding

  1. Phase-I Fellowship of the National Science Foundation (NSF) Molecular Sciences Software Institute at Virginia Tech [ACI-1547580-479590]
  2. Phase-II Software Fellowship of the National Science Foundation (NSF) Molecular Sciences Software Institute at Virginia Tech [ACI-1547580-479590]
  3. NSF CAREER program [OAC-1751161]
  4. NSF Big Data Spokes program [IIS-1761990]
  5. New York State Center of Excellence in Materials Informatics [CMI1148092-8-75163]

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In this review, we highlight recent developments in the application of machine learning for molecular modeling and simulation. After giving a brief overview of the foundations, components, and workflow of a typical supervised learning approach for chemical problems, we showcase areas and state-of-the-art examples of their deployment. In this context, we discuss how machine learning relates to, supports, and augments more traditional physics-based approaches in computational research. We conclude by outlining challenges and future research directions that need to be addressed in order to make machine learning a mainstream chemical engineering tool.

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