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説明:

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 3d transition metal–4f 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.

権利情報:

• Creative Commons BY Attribution 4.0 International
  Creative Commons BY Attribution 4.0 International

キーワード: Dempster–Shafer theory, similarity evidence, evidence theory, Curie temperature, visualization, physical mechanism, mixture of experts, transition rare-earth metal binary allloys, magnetization

刊行年月日: 2023-02-07

出版者:

掲載誌:

• JAPANESE JOURNAL OF APPLIED PHYSICS (ISSN: 00214922) vol. 133 p. 53904-53904

研究助成金:

• MEXT the Program for Promoting Research on the Supercomputer Fugaku (DPMSD)
• JSPS KAKENHI 20K05301
• JSPS KAKENHI 20K05301
• Grant-in-Aid for Early- Career Scientists 21K14396
• Grant-in-Aid for Early- Career Scientists 20K05068
• Grants-in-Aid for Scientific Research on Innovative Areas Interface Ionics JP19H05815

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1063/5.0134999

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更新時刻: 2024-01-05 22:13:49 +0900

MDRでの公開時刻: 2023-02-08 11:15:47 +0900