Combinatorial screening for new materials in unconstrained composition space with machine learning

B. Meredig*, Ankit Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, Alok Nidhi Choudhary, Christopher M Wolverton

*Corresponding author for this work

Research output: Contribution to journalArticle

239 Scopus citations

Abstract

Typically, computational screens for new materials sharply constrain the compositional search space, structural search space, or both, for the sake of tractability. To lift these constraints, we construct a machine learning model from a database of thousands of density functional theory (DFT) calculations. The resulting model can predict the thermodynamic stability of arbitrary compositions without any other input and with six orders of magnitude less computer time than DFT. We use this model to scan roughly 1.6 million candidate compositions for novel ternary compounds (AxByCz), and predict 4500 new stable materials. Our method can be readily applied to other descriptors of interest to accelerate domain-specific materials discovery.

Original languageEnglish (US)
Article number094104
JournalPhysical Review B - Condensed Matter and Materials Physics
Volume89
Issue number9
DOIs
StatePublished - Mar 14 2014

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Electronic, Optical and Magnetic Materials

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