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

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.

13 pages and 5 figures, Chem. Mater., 2015

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Cited by in corpus (5)

• Learning physical descriptors for materials science by compressed sensing
• Data Mining Graphene: Correlative Analysis of Structure and Electronic Degrees of Freedom in Graphenic Monolayers with Defects
• AFLOW$\pi$: A minimalist approach to high-throughput ab initio calculations including the generation of tight-binding hamiltonians
• AFLUX: The LUX materials search API for the AFLOW data repositories
• Chemical Bond-Based Representation of Materials