Density-of-states similarity descriptor for unsupervised learning from materials data

2022/01/06 by Martin Kuban, Santiago Rigamonti, Kuban, Martin +5 · 1 citation
Physics and Astronomy · #FOS: Physical sciences #Materials Science (cond-mat.mtrl-sci) #cond-mat.mtrl-sci

paper · pdf · doi:10.48550/arxiv.2201.02187

arxiv created 2022/01/06 · arxiv updated 2022/01/07

Abstract

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.

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• AIM2DAT: A Python-based Automated Ab Initio Material Modeling and Data Analysis Toolkit

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