Genetic Algorithms for the Discovery of Homogeneous Catalysts
Keywords:Catalysis, Discovery, Homogeneous, Machine learning
In this account, we discuss the use of genetic algorithms in the inverse design process of homogeneous catalysts for chemical transformations. We describe the main components of evolutionary experiments, specifically the nature of the fitness function to optimize, the library of molecular fragments from which potential catalysts are assembled, and the settings of the genetic algorithm itself. While not exhaustive, this review summarizes the key challenges and characteristics of our own (i.e., NaviCatGA) and other GAs for the discovery of new catalysts.
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Copyright (c) 2023 Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, Ruben Laplaza, Clemence Corminboeuf
This work is licensed under a Creative Commons Attribution 4.0 International License.