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Solving the Colouring Problem in Half-Heusler Structures: Machine-Learning Predictions and Experimental Validation

preprint
submitted on 27.03.2019, 19:25 and posted on 28.03.2019, 15:17 by Alexander Gzyl, Anton Oliynyk, Lawrence Adutwum, Arthur Mar
The site preferences within the structures of half-Heusler compounds have been evaluated through a machine-learning approach. A support-vector machine algorithm was applied to develop a model which was trained on 179 experimentally reported structures and 23 descriptors based solely on the chemical composition. The model gave excellent performance, with sensitivity of 93%, selectivity of 96%, and accuracy of 95%. As an illustration of data sanitization, two compounds (GdPtSb, HoPdBi) flagged by the model to have potentially incorrect site assignments were resynthesized and structurally characterized. The predictions of the correct site assignments from the machine-learning model were confirmed by single-crystal and powder X-ray diffraction analysis. These site assignments also corresponded to the lowest total energy configurations as revealed from first-principles calculations.

History

Email Address of Submitting Author

arthur.mar@ualberta.ca

Institution

University of Alberta

Country

Canada

ORCID For Submitting Author

0000-0003-0474-5918

Declaration of Conflict of Interest

No conflict of interest

Version Notes

March 27, 2019 version.

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