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

TL;DR

This paper introduces a novel materials descriptor based on electronic density of states to analyze and cluster 2D materials by their electronic structure, aiding materials discovery.

Contribution

The work presents a new density-of-states-based similarity descriptor and demonstrates its effectiveness in clustering 2D materials from a large database.

Findings

Successfully clusters materials with similar electronic structures

Reveals relationships between crystal structure and electronic properties

Provides insights into electronic configuration similarities

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.