期刊
CURRENT OPINION IN CHEMICAL ENGINEERING
卷 23, 期 -, 页码 51-57出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.coche.2019.02.009
关键词
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资金
- Phase-I Fellowship of the National Science Foundation (NSF) Molecular Sciences Software Institute at Virginia Tech [ACI-1547580-479590]
- Phase-II Software Fellowship of the National Science Foundation (NSF) Molecular Sciences Software Institute at Virginia Tech [ACI-1547580-479590]
- NSF CAREER program [OAC-1751161]
- NSF Big Data Spokes program [IIS-1761990]
- New York State Center of Excellence in Materials Informatics [CMI1148092-8-75163]
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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