  We showcase the potential of 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. We then present two GPSR use-cases: formulating a
transformation kinetics law and showing the learning scheme discovers the
well-known Johnson-Mehl-Avrami-Kolmogorov (JMAK) form, and learning the Landau
free energy functional form for the displacive tilt transition in perovskite
LaNiO$_3$. 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.
