Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists

Freeze, Jessica G.; Kelly, H. Ray; Batista, Victor S.

doi:10.1021/acs.chemrev.8b00759

Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists

Journal Article · Mon May 06 00:00:00 EDT 2019 · Chemical Reviews

DOI:https://doi.org/10.1021/acs.chemrev.8b00759· OSTI ID:1577490

Freeze, Jessica G. ^[1]; Kelly, H. Ray ^[1]; ^[1]

Yale Univ., New Haven, CT (United States); Yale Univ., West Haven, CT (United States)

In silico catalyst design is a grand challenge of chemistry. Traditional computational approaches have been limited by the need to compute properties for an intractably large number of possible catalysts. Recently, inverse design methods have emerged, starting from a desired property and optimizing a corresponding chemical structure. Techniques used for exploring chemical space include gradient-based optimization, alchemical transformations, and machine learning. Though the application of these methods to catalysis is in its early stages, further development will allow for robust computational catalyst design. This review provides an overview of the evolution of inverse design approaches and their relevance to catalysis. In conclusion, the strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: AFOSR; National Science Foundation; USDOE Office of Science (SC)

OSTI ID:: 1577490

Journal Information:: Chemical Reviews, Journal Name: Chemical Reviews Journal Issue: 11 Vol. 119; ISSN 0009-2665

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

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