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Symbolic regression in materials science

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Abstract

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.

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Acknowledgments

Y.W. acknowledges partial support from the Predictive Science and Engineering Design (PS&ED) program at Northwestern University. All authors acknowledge support from the National Science Foundation (NSF) through the Designing Materials to Revolutionize and Engineer our Future (DMREF) program under award no. DMR-1729303.

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  1. Department of Materials Science and Engineering, Northwestern University, Evanston, IL, 60208, USA

    Yiqun Wang, Nicholas Wagner & James M. Rondinelli

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  1. Yiqun Wang
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Correspondence to James M. Rondinelli.

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Wang, Y., Wagner, N. & Rondinelli, J.M. Symbolic regression in materials science. MRS Communications 9, 793–805 (2019). https://doi.org/10.1557/mrc.2019.85

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