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- W2943843211 endingPage "6612" @default.
- W2943843211 startingPage "6595" @default.
- W2943843211 abstract "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. The strengths and limitations of existing techniques are highlighted, and suggestions for future research are provided." @default.
- W2943843211 created "2019-05-16" @default.
- W2943843211 creator A5011517597 @default.
- W2943843211 creator A5065641804 @default.
- W2943843211 creator A5089129603 @default.
- W2943843211 date "2019-05-06" @default.
- W2943843211 modified "2023-10-17" @default.
- W2943843211 title "Search for Catalysts by Inverse Design: Artificial Intelligence, Mountain Climbers, and Alchemists" @default.
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