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

B. Meredig, A. Agrawal, S. Kirklin, J. E. Saal, J. W. Doak, A. Thompson, K. Zhang, A. Choudhary, and C. Wolverton
Phys. Rev. B 89, 094104 – Published 14 March 2014
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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.

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  • Received 2 November 2013

DOI:https://doi.org/10.1103/PhysRevB.89.094104

©2014 American Physical Society

Authors & Affiliations

B. Meredig1,*, A. Agrawal2,*, S. Kirklin1, J. E. Saal1, J. W. Doak1, A. Thompson1, K. Zhang2, A. Choudhary2, and C. Wolverton1

  • 1Department of Materials Science and Engineering, Northwestern University, Evanston, Illinois 60208, USA
  • 2Department of Electrical Engineering and Computer Science, Northwestern University, Evanston, Illinois 60208, USA

  • *These authors contributed equally to this work.

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Vol. 89, Iss. 9 — 1 March 2014

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