委员会的机器，投票的相似性之间的材料
Committee machine that votes for similarity between materials
摘要：
我们开发了一种测量材料之间相似性的方法，专注于特定的物理特性。所获得的信息可以用来理解潜在的机制，并支持材料的物理性质的预测。该方法由三个步骤组成：基于非线性回归的变量评估，基于回归的聚类，和相似性度量与委员会机构造的聚类结果。三个数据集的表征晶体材料的临界原子预测变量被用作测试床。在这里，我们集中在形成能，晶格参数，和居里温度的检查材料。基于所获得的材料之间的相似性的信息，应用层次聚类技术来学习的集群结构的材料，便于解释的机制，并引入了改进的回归模型来预测材料的物理性能。实验结果表明，该相似性度量方法可以得到合理的、有意义的组结构，并能显著提高材料物理性能的预测精度，从而验证了该相似性度量方法的合理性。
英文摘要：
We developed a method for measuring the similarity between materials, focusing on specific physical properties. The obtained information can be utilized to understand the underlying mechanisms and to support the prediction of the physical properties of materials. The method consists of three steps: variable evaluation based on non-linear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three datasets of well-characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, we focus 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 interpreting the mechanism, and an improvement of regression models is introduced for predicting the physical properties of the materials. Our 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
