Nguyen Duong-Nguyen, Pham Tien-Lam, Nguyen Viet-Cuong, Ho Tuan-Dung, Tran Truyen, Takahashi Keisuke, Dam Hieu-Chi
Japan Advanced Institute of Science and Technology, 1-1 Asahidai, Nomi, Ishikawa 923-1292, Japan.
ESICMM, National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047, Japan.
IUCrJ. 2018 Oct 30;5(Pt 6):830-840. doi: 10.1107/S2052252518013519. eCollection 2018 Nov 1.
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.
已经开发出一种方法来测量材料之间的相似性，重点关注特定的物理性质。所获得的信息可用于理解潜在机制并支持对材料物理性质的预测。该方法包括三个步骤：基于非线性回归的变量评估、基于回归的聚类以及使用由聚类结果构建的委员会机器进行相似性测量。以关键原子预测变量表示的三个特征明确的晶体材料数据集用作测试平台。在此，重点是所研究材料的形成能、晶格参数和居里温度。基于所获得的材料之间相似性的信息，应用层次聚类技术来学习有助于机制解释的材料聚类结构，并引入回归模型的改进来预测材料的物理性质。实验表明，可以获得合理且有意义的组结构，并且材料物理性质的预测准确性可以显著提高，证实了所提出的相似性度量的合理性。
