{
  "id": "1901.04136",
  "version": "v1",
  "published": "2019-01-14T05:31:27.000Z",
  "updated": "2019-01-14T05:31:27.000Z",
  "title": "Symbolic Regression in Materials Science",
  "authors": [
    "Yiqun Wang",
    "Nicholas Wagner",
    "James M. Rondinelli"
  ],
  "comment": "12 pages, 5 figures, Comments and suggestions welcome",
  "categories": [
    "cond-mat.mtrl-sci",
    "physics.comp-ph"
  ],
  "abstract": "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.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2019-01-14T05:31:27.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "materials science",
      "symbolic regression techniques",
      "current state-of-the-art method",
      "genetic programming-based symbolic regression",
      "transformation kinetics law"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 12,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}