Symbolic Regression in Materials Science

Yiqun Wang, Nicholas Wagner, James M. Rondinelli

Published 2019-01-14Version 1

We introduce symbolic regression as an analytic method for use in materials research. First, we briefly describe the current state-of-the-art method, genetic programming-based symbolic regression (GPSR), and recent advances in symbolic regression techniques. Next, we discuss industrial applications of symbolic regression and its potential applications in materials science. Last, we present a GPSR use-case for formulating a transformation kinetics law and show the learning scheme discovers the well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form. Finally, we 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.