Global mapping of structures and properties of crystal materials

TL;DR

This paper presents a comprehensive global mapping approach to visualize and analyze the distribution of crystal materials and their properties, revealing fundamental relationships and aiding in new material discovery.

Contribution

It introduces a novel methodology for creating global maps of materials based on multiple descriptors, enabling visualization of material distributions and property relationships.

Findings

Materials form distinct clusters in the maps.

Material properties correlate with structural and compositional patterns.

The approach facilitates new material discovery and understanding of structure-property relationships.

Abstract

Understanding material composition-structure-function relationships is of critical importance for the design and discovery of novel functional materials. While most such studies focus on individual materials, we conducted a global mapping study of all known materials deposited in the Material Project database to investigate their distributions in the space of a set of seven compositional, structural, physical, and neural latent descriptors. These two-dimensional materials maps along with their density maps allow us to illustrate the distribution of the patterns and clusters of different shapes, which indicates the propensity of these materials and the tinkering history of existing materials. We then overlap the material properties such as composition prototypes and piezoelectric properties over the background materials maps to study the relationships of how material compositions and structures affect their physical properties. We also use these maps to study the spatial distributions of properties of known inorganic materials, in particular those of local vicinities in structural space such as structural density and functional diversity. These maps provide a uniquely comprehensive overview of materials and space and thus reveal previously undescribed fundamental properties. Our methodology can be easily extended by other researchers to generate their own global material maps with different background maps and overlap properties for both distribution understanding and cluster-based new material discovery. The source code for feature generation and generated maps are available at https://github.com/usccolumbia/matglobalmapping