Theory-guided machine learning in materials science

Nicholas Wagner, James M. Rondinelli*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

54 Scopus citations

Abstract

Materials scientists are increasingly adopting the use of machine learning tools to discover hidden trends in data and make predictions. Applying concepts from data science without foreknowledge of their limitations and the unique qualities of materials data, however, could lead to errant conclusions. The differences that exist between various kinds of experimental and calculated data require careful choices of data processing and machine learning methods. Here, we outline potential pitfalls involved in using machine learning without robust protocols. We address some problems of overfitting to training data using decision trees as an example, rational descriptor selection in the field of perovskites, and preserving physical interpretability in the application of dimensionality reducing techniques such as principal component analysis. We show how proceeding without the guidance of domain knowledge can lead to both quantitatively and qualitatively incorrect predictive models.

Original languageEnglish (US)
Article number28
JournalFrontiers in Materials
Volume3
DOIs
StatePublished - Jun 27 2016

Keywords

  • Descriptor selection
  • Machine learning
  • Materials informatics
  • Overfitting
  • Theory

ASJC Scopus subject areas

  • Materials Science (miscellaneous)

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