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Symbolic regression in materials science

Published online by Cambridge University Press:  21 June 2019

Yiqun Wang ,
Nicholas Wagner  and
James M. Rondinelli Open the ORCID record for James M. Rondinelli [Opens in a new window]
Yiqun Wang
Affiliation:
Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA
Nicholas Wagner
Affiliation:
Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA
James M. Rondinelli*
Affiliation:
Department of Materials Science and Engineering, Northwestern University, Evanston, IL 60208, USA
*
Address all correspondence to James M. Rondinelli at jrondinelli@northwestern.edu
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Abstract

The authors showcase the potential of symbolic regression as an analytic method for use in materials research. First, the authors briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, the authors discuss industrial applications of symbolic regression and its potential applications in materials science. The authors then present two GPSR use-cases: formulating a transformation kinetics law and showing the learning scheme discovers the well-known Johnson–Mehl–Avrami–Kolmogorov form, and learning the Landau free energy functional form for the displacive tilt transition in perovskite LaNiO3. Finally, the authors propose that symbolic regression techniques should be considered by materials scientists as an alternative to other machine learning-based regression models for learning from data.

Type
Artificial Intelligence Prospectives
Information
MRS Communications , Volume 9 , Issue 3 , September 2019 , pp. 793 - 805
Copyright
Copyright © Materials Research Society 2019 

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Footnotes

†

These authors contributed equally to this work.

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