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

28 March 2019, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


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.


crystal structure
data mining
Half-Heusler compounds
machine learning

Supplementary materials

Half Heusler ML SI


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