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Article Dans Une Revue Journal of Applied Physics Année : 2023

Evidence-based data mining method to reveal similarities between materials based on physical mechanisms

Résumé

Measuring the similarity between materials is essential for estimating their properties and revealing the associated physical mechanisms. However, current methods for measuring the similarity between materials rely on theoretically derived descriptors and parameters fitted from experimental or computational data, which are often insufficient and biased. Furthermore, outliers and data generated by multiple mechanisms are usually included in the dataset, making the data-driven approach challenging and mathematically complicated. To overcome such issues, we apply the Dempster–Shafer theory to develop an evidential regression-based similarity measurement (eRSM) method, which can rationally transform data into evidence. It then combines such evidence to conclude the similarities between materials, considering their physical properties. To evaluate the eRSM, we used two material datasets, including 3[Formula: see text] transition metal–4[Formula: see text] rare-earth binary and quaternary high-entropy alloys with target properties, Curie temperature, and magnetization. Based on the information obtained on the similarities between the materials, a clustering technique is applied to learn the cluster structures of the materials that facilitate the interpretation of the mechanism. The unsupervised learning experiments demonstrate that the obtained similarities are applicable to detect anomalies and appropriately identify groups of materials whose properties correlate differently with their compositions. Furthermore, significant improvements in the accuracies of the predictions for the Curie temperature and magnetization of the quaternary alloys are obtained by introducing the similarities, with the reduction in mean absolute errors of 36% and 18%, respectively. The results show that the eRSM can adequately measure the similarities and dissimilarities between materials in these datasets with respect to mechanisms of the target properties.
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Dates et versions

hal-03976318 , version 1 (07-02-2023)

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Minh-Quyet Ha, Duong-Nguyen Nguyen, Viet-Cuong Nguyen, Hiori Kino, Yasunobu Ando, et al.. Evidence-based data mining method to reveal similarities between materials based on physical mechanisms. Journal of Applied Physics, 2023, 133 (5), pp.053904. ⟨10.1063/5.0134999⟩. ⟨hal-03976318⟩
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