Genetic Algorithms for the Discovery of Homogeneous Catalysts

Authors

  • Simone Gallarati Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; https://orcid.org/0000-0002-2349-1944
  • Puck van Gerwen Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland https://orcid.org/0000-0002-7992-5529
  • Alexandre A. Schoepfer Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland
  • Ruben Laplaza Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland https://orcid.org/0000-0001-6315-4398
  • Clemence Corminboeuf Laboratory for Computational Molecular Design, Institut des Sciences et Ingénierie Chimiques, École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland; National Center for Competence in Research-Catalysis (NCCR-Catalysis), École Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland https://orcid.org/0000-0001-7993-2879

DOI:

https://doi.org/10.2533/chimia.2023.39

PMID:

38047852

Keywords:

Catalysis, Discovery, Homogeneous, Machine learning

Abstract

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.

Funding data

CHIMIA Vol. 77 No. 1/2 (2023): Artificial Intelligence in Chemical Research

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Published

2023-02-22

How to Cite

[1]
S. Gallarati, P. van Gerwen, A. A. Schoepfer, R. Laplaza, C. Corminboeuf, Chimia 2023, 77, 39, DOI: 10.2533/chimia.2023.39.

Issue

Vol. 77 No. 1/2 (2023): Artificial Intelligence in Chemical Research

Section

Scientific Articles

License

Copyright (c) 2023 Simone Gallarati, Puck van Gerwen, Alexandre A. Schoepfer, Ruben Laplaza, Clemence Corminboeuf

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.