  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.
