Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment

Research output: Contribution to journalReview articlepeer-review

5 Scopus citations

Abstract

Genetic algorithms (GA) and machine learning (ML) have a long history of development and use in chemistry. Recent algorithmic and computational advances, however, have brought these methods to the forefront of chemical research, and chemistry is experiencing a transformation in the way that machines and humans interact to pursue scientific advances. The field of materials chemistry, in particular, has witnessed a considerable expansion in the maturity of GA and ML approaches, as machine-based materials design ushers in a new era of materials development, discovery, and deployment. In addition to predicting new compositions and properties of bulk materials, GA and ML have also guided new insights into the structure, composition, and chemistry of materials surfaces. In this review, we focus on how GA and ML have been used in conjunction with chemical simulation techniques to advance understanding of surface chemistry, examining the history, recent work, and overall success of these applications.

Original languageEnglish
Pages (from-to)6589-6615
Number of pages27
JournalChemistry of Materials
Volume33
Issue number17
DOIs
StatePublished - Sep 14 2021

Bibliographical note

Funding Information:
This work was funded by the Research Corporation for Science Advancement (RCSA) Cottrell Scholars Program (Award No. 24432).

Publisher Copyright:
© 2021 American Chemical Society.

ASJC Scopus subject areas

  • Chemistry (all)
  • Chemical Engineering (all)
  • Materials Chemistry

Fingerprint

Dive into the research topics of 'Genetic Algorithms and Machine Learning for Predicting Surface Composition, Structure, and Chemistry: A Historical Perspective and Assessment'. Together they form a unique fingerprint.

Cite this