Towards Novel Organic High-$T_\mathrm{c}$ Superconductors: Data Mining using Density of States Similarity Search
R. Matthias Geilhufe, Stanislav S. Borysov, Dmytro Kalpakchi and, Alexander V. Balatsky

TL;DR
This paper introduces a novel density of states similarity search tool for organic materials, aiding the discovery of potential high-temperature superconductors by identifying materials with electronic structures similar to known candidates.
Contribution
The paper presents a new density of states similarity search method using approximate nearest neighbor algorithms applied to the OMDB database, enabling efficient identification of materials with similar electronic properties.
Findings
Identified 15 organic materials with electronic structures similar to p-terphenyl.
Demonstrated the tool's applicability for discovering materials with potential high-$T_c$ superconductivity.
Provided a web interface for accessible similarity searches in the OMDB database.
Abstract
Identifying novel functional materials with desired key properties is an important part of bridging the gap between fundamental research and technological advancement. In this context, high-throughput calculations combined with data-mining techniques highly accelerated this process in different areas of research during the past years. The strength of a data-driven approach for materials prediction lies in narrowing down the search space of thousands of materials to a subset of prospective candidates. Recently, the open-access organic materials database OMDB was released providing electronic structure data for thousands of previously synthesized three-dimensional organic crystals. Based on the OMDB, we report about the implementation of a novel density of states similarity search tool which is capable of retrieving materials with similar density of states to a reference material. The…
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