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

Martin Kuban; Santiago Rigamonti; Markus Scheidgen; Claudia Draxl

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

Materials Science 2022-01-07 v1

Authors: Martin Kuban , Santiago Rigamonti , Markus Scheidgen , Claudia Draxl

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.

Keywords

crystal structure prediction atomic clusters and nuclear clustering large-scale structure of the universe

Cite

@article{arxiv.2201.02187,
  title  = {Density-of-states similarity descriptor for unsupervised learning from materials data},
  author = {Martin Kuban and Santiago Rigamonti and Markus Scheidgen and Claudia Draxl},
  journal= {arXiv preprint arXiv:2201.02187},
  year   = {2022}
}

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