These are preliminary reports that have not been peer-reviewed. They should not be regarded as conclusive, guide clinical practice/health-related behavior, or be reported in news media as established information. For more information, please see our FAQs.
Preprints are manuscripts made publicly available before they have been submitted for formal peer review and publication. They might contain new research findings or data. Preprints can be a draft or final version of an author's research but must not have been accepted for publication at the time of submission.
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.