Materials Cartography: Representing and Mining Material Space Using Structural and Electronic Fingerprints

TL;DR

This paper introduces novel structural and electronic fingerprinting methods for materials data, enabling similarity searches, mapping material space, and predicting properties like superconducting temperatures to advance materials informatics.

Contribution

It presents new fingerprinting and cartography techniques for materials databases, facilitating discovery, visualization, and property prediction in materials science.

Findings

Effective similarity-based querying of materials databases.

Successful mapping of materials space revealing unique regions.

Accurate modeling of superconducting critical temperatures.

Abstract

As the proliferation of high-throughput approaches in materials science is increasing the wealth of data in the field, the gap between accumulated-information and derived-knowledge widens. We address the issue of scientific discovery in materials databases by introducing novel analytical approaches based on structural and electronic materials fingerprints. The framework is employed to (i) query large databases of materials using similarity concepts, (ii) map the connectivity of the materials space (i.e., as a materials cartogram) for rapidly identifying regions with unique organizations/properties, and (iii) develop predictive Quantitative Materials Structure-Property Relation- ships (QMSPR) models for guiding materials design. In this study, we test these fingerprints by seeking target material properties. As a quantitative example, we model the critical temperatures of known superconductors. Our novel materials fingerprinting and materials cartography approaches contribute to the emerging field of materials informatics by enabling effective computational tools to analyze, visualize, model, and design new materials.