论文标题
从材料数据中无监督学习的状态密度相似性描述符
Density-of-states similarity descriptor for unsupervised learning from materials data
论文作者
论文摘要
我们根据状态的电子密度开发材料描述符，并根据其研究材料的相似性。作为应用程序示例，我们研究了计算2D材料数据库，该数据库托管了数千种二维材料，其属性由密度功能理论计算得出。将我们的描述符与聚类算法相结合，我们确定具有相似电子结构的材料组。我们从它们的晶体结构，原子组成和相应的电子构型来表征这些簇，以合理化发现（DIS）相似性。
We develop a materials descriptor based on the electronic density of states and investigate the similarity of materials based on it. As an application example, we study the Computational 2D Materials Database that hosts thousands of two-dimensional materials with their properties calculated by density-functional theory. Combining our descriptor with a clustering algorithm, we identify groups of materials with similar electronic structure. We characterize these clusters in terms of their crystal structure, their atomic composition, and the respective electronic configurations to rationalize the found (dis)similarities.
