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

TLDR

High‑throughput materials science generates vast data, widening the gap between data accumulation and derived knowledge. The study introduces and tests structural and electronic fingerprinting methods to discover materials with target properties. The authors develop a fingerprint‑based framework that queries databases, maps materials space, and builds predictive models, exemplified by modeling superconducting critical temperatures. These fingerprinting and cartography methods advance materials informatics by providing tools to analyze, visualize, model, and design new materials.

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 materials space (i.e., as a materials cartograms) for rapidly identifying regions with unique organizations/properties, and (iii) develop predictive Quantitative Materials Structure–Property Relationship 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.