Online Search Tool for Graphical Patterns in Electronic Band Structures

Abstract

We present an online graphical pattern search tool for electronic band structure data contained within the Organic Materials Database (OMDB) available at https://omdb.diracmaterials.org/search/pattern. The tool is capable of finding user-specified graphical patterns in the collection of thousands of band structures from high-throughput ab initio calculations in the online regime. Using this tool, it only takes a few seconds to find an arbitrary graphical pattern within the ten electronic bands near the Fermi level for 26,739 organic crystals. The tool can be used to find realizations of functional materials characterized by a specific pattern in their electronic structure, for example, Dirac materials, characterized by a linear crossing of bands; topological insulators, characterized by a "Mexican hat" pattern or an effectively free electron gas, characterized by a parabolic dispersion. The source code of the developed tool is freely available at https://github.com/OrganicMaterialsDatabase/EBS-search and can be transferred to any other electronic band structure database. The approach allows for an automatic online analysis of a large collection of band structures where the amount of data makes its manual inspection impracticable.

8 pages, 8 figures

References in corpus (5)

• Topological quantum chemistry
• Computational 2D Materials Database: Electronic Structure of Transition-Metal Dichalcogenides and Oxides
• Global band topology of simple and double Dirac-point (semi-)metals
• Towards Novel Organic High-$T_{\mathrm{c}}$ Superconductors: Data Mining using Density of States Similarity Search
• Combining electronic structure and many-body theory with large data-bases: a method for predicting the nature of 4f states in Ce compounds

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