A representation-independent electronic charge density database for crystalline materials

TL;DR

This paper introduces a comprehensive, representation-independent charge density database for crystalline materials, facilitating advanced machine learning applications and detailed materials analysis.

Contribution

It provides a large, continuously updated charge density database with tools for changing representations, enhancing machine learning and analysis in materials science.

Findings

Database contains extensive charge density data for crystalline materials.

Tools enable changing charge density representations for diverse analyses.

Supports advanced machine learning applications in materials science.

Abstract

In addition to being the core quantity in density functional theory, the charge density can be used in many tertiary analyses in materials sciences from bonding to assigning charge to specific atoms. The charge density is data-rich since it contains information about all the electrons in the system. With increasing utilization of machine-learning tools in materials sciences, a data-rich object like the charge density can be utilized in a wide range of applications. The database presented here provides a modern and user-friendly interface for a large and continuously updated collection of charge densities as part of the Materials Project. In addition to the charge density data, we provide the theory and code for changing the representation of the charge density which should enable more advanced machine-learning studies for the broader community.