材料数据无监督学习的状态密度相似描述子
Density-of-states similarity descriptor for unsupervised learning from materials data
摘要：
我们开发了一个基于电子态密度的材料描述子，并在此基础上研究了材料的相似性。作为应用实例，我们研究了计算二维材料数据库，该数据库包含数千种二维材料，并利用密度泛函理论计算了它们的性质。结合我们的描述符与聚类算法，我们确定具有相似电子结构的材料组。我们表征这些集群在他们的晶体结构，原子组成，和各自的电子配置合理化发现（不）相似性。
英文摘要：
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.
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。
