Symbolic regression in materials science

Yiqun Wang, Nicholas Wagner, James M. Rondinelli*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

122 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)793-805
Number of pages13
JournalMRS Communications
Issue number3
StatePublished - Sep 1 2019

ASJC Scopus subject areas

  • General Materials Science


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