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Please use this identifier to cite or link to this item:
http://hdl.handle.net/10119/16010
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| Title: | Committee machine that votes for similarity between materials |
| Authors: | Nguyen, Duong-Nguyen
Pham, Tien-Lam
Nguyen, Viet-Cuong
Ho, Tuan-Dung
Tran, Truyen
Takahashi, Keisuke
Dam, Hieu-Chi |
| Keywords: | Materials Informatics
Machine learning
Data mining
Similarity measure |
| Issue Date: | 2018-10-30 |
| Publisher: | International Union of Crystallography |
| Magazine name: | IUCrJ |
| Volume: | 5 |
| Number: | 6 |
| Start page: | 830 |
| End page: | 840 |
| DOI: | 10.1107/S2052252518013519 |
| Abstract: | A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure. |
| Rights: | Duong-Nguyen Nguyen, Tien-Lam Pham, Viet-Cuong Nguyen, Tuan-Dung Ho, Truyen Tran, Keisuke Takahashi and Hieu-Chi Dam, IUCrJ, 5(6), 2018, 830-840. DOI:10.1107/S2052252518013519. (c) Copyright 2018 Author(s). This article is distributed under a Creative Commons Attribution (CC-BY) License. |
| URI: | http://hdl.handle.net/10119/16010 |
| Material Type: | publisher |
| Appears in Collections: | a10-1. 雑誌掲載論文 (Journal Articles)
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| 24530.pdf | | 1265Kb | Adobe PDF | View/Open |
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