Committee machine that votes for similarity between materials

IUCrJ

Ho, T.-D.¹  Tran, T.²  Pham, T.-L.³  Nguyen, V.-C.⁴  Nguyen, D.-N.⁵ 

[1] Applied Artificial Intelligence Institute, Deakin University, Geelong, Australia;Center for Materials Research by Information Integration, National Institute for Materials Science 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan;ESICMM, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan;HPC Systems Inc., 3-9-15 Kaigan, Minato-ku, Tokyo 108-0022, Japan;Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan

关键词: DATA MINING;    MATERIALS INFORMATICS;    FIRST-PRINCIPLES CALCULATIONS;    PHYSICAL PROPERTIES OF MATERIALS;    MACHINE LEARNING;    SIMILARITY;   

DOI  :  10.1107/S2052252518013519

学科分类：数学（综合）

来源: International Union of Crystallography

【 摘 要 】

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.

【 授权许可】

CC BY