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 analytic approaches based on structural and electronic materials fingerprints. The framework is employed to:
- Query large databases of materials using similarity concepts.
- Map the connectivity of materials space (i.e., as a materials cartograms) for rapidly identifying regions with unique organizations/properties.
- Develop predictive Quantitative Materials Structure–Property Relationship models for guiding materials design.
We have introduced novel materials descriptors that encode band structures (B-fingerprints), density of states (D-fingerprints), as well as crystallographic and constitutional information of materials. We employed materials fingerprints to visualize large collections of materials as a contact network, or Materials Cartograms. In the cartogram, the nodes of the network are individual materials, and similar materials are connected by an edge. Two materials (nodes) are connected only when the similarity of their fingerprints is above certain threshold.