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High-Throughput Machine-Learning-Driven Synthesis of Full-Heusler Compounds

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Department of Chemistry, University of Alberta, Edmonton, AB T6G 2G2 Canada
Citrine Informatics, Redwood City, California 94063 United States
§ Department of Materials Science and Engineering, University of Utah, Salt Lake City, Utah 84112 United States
Department of Chemistry, University of Cambridge, Cambridge CB2 1EW United Kingdom
Cite this: Chem. Mater. 2016, 28, 20, 7324–7331
Publication Date (Web):September 18, 2016
https://doi.org/10.1021/acs.chemmater.6b02724
Copyright © 2016 American Chemical Society

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    Abstract

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    A machine-learning model has been trained to discover Heusler compounds, which are intermetallics exhibiting diverse physical properties attractive for applications in thermoelectric and spintronic materials. Improving these properties requires knowledge of crystal structures, which occur in three subtle variations (Heusler, inverse Heusler, and CsCl-type structures) that are difficult, and at times impossible, to distinguish by diffraction techniques. Compared to alternative approaches, this Heusler discovery engine performs exceptionally well, making fast and reliable predictions of the occurrence of Heusler vs non-Heusler compounds for an arbitrary combination of elements with no structural input on over 400 000 candidates. The model has a true positive rate of 0.94 (and false positive rate of 0.01). It is also valuable for data sanitizing, by flagging questionable entries in crystallographic databases. It was applied to screen candidates with the formula AB2C and predict the existence of 12 novel gallides MRu2Ga and RuM2Ga (M = Ti–Co) as Heusler compounds, which were confirmed experimentally. One member, TiRu2Ga, exhibited diagnostic superstructure peaks that confirm the adoption of an ordered Heusler as opposed to a disordered CsCl-type structure.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.chemmater.6b02724.

    • Machine-learning-predicted probabilities for the most likely Heusler and non-Heusler compounds; predicted probabilities for MRu2Ga, RuM2Ga, and LaM2Ga; probability distribution plots using 10-fold cross-validation and receiver operating characteristic curves; powder XRD analyses for ternary gallides; single-crystal XRD analysis for TiRu2Ga; and SEM/EDX analysis for TiRu2Ga (PDF)

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