Gene

Transformation (genetics)

Engineering

Psychoanalysis

Biology

Statistics

Materials science

Symbolic regression

Genetic programming

Theoretical computer science

Biochemistry

Computer science

Lanio

Ferroelectricity

Statistical physics

Regression analysis

Physics

Regression

Dielectric

Psychology

Mathematics

The Symbolic

Artificial intelligence

Chemistry

Optoelectronics

Machine learning

Electrical engineering

MRS Communications

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.

Symbolic regression in materials science