2019/01/31 by Yiqun Wang, Nicholas Wagner, James M. Rondinelli · 2 citations
Computer Science · Materials Science · Physics and Astronomy · Social Sciences · #Evolutionary Algorithms and Applications #Genetic programming #Language and cultural evolution #Machine Learning in Materials Science #Regression #Regression analysis #Symbolic regression #The Symbolic #Transformation (genetics) #cond-mat.mtrl-sci #physics.comp-ph
paper · pdf · doi:10.1557/mrc.2019.85
published as MRC 9 (2019) 793-805 · 14 pages, 6 figures
openalex created_date 2019/01/25 · arxiv created 2019/05/16 · openalex publication_date 2019/06/21 · arxiv updated 2019/10/02 · openalex updated_date 2026/08/06
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 LaNiO3. 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.