Organic Materials Database

Visit the Organic Materials Database (external link)

APS Editorial office, Ridge, NY, talk on “Dirac materials and Organic materials database” (Nov 1, 2017)

We have developed the organic materials database (omdb.mathub.io)., a free and open access electronic and magnetic structure database for about 25,000 previously synthesized 3-dimensional organic crystals. The contained information are calculated within our group by means of ab initio methods based on density functional theory. We develop tools for search queries based on data mining and machine learning techniques. The universal features provided on our web interface facilitate the identification of functional organic materials for a wide-range of applications.

The OMDB provides data-driven online services specifically for organic materials, a class of high technological relevance. The elasticity of these compounds connected to the endless configuration space and tuning opportunities opens the path for various basic research and industrial applications. Specifically, we have applied the organic materials database to identify organic Dirac  and line node materials, sensor materials for dark matter detection, and high-temperature organic superconductors.

The OMDB community provides additional resources and services such as computational support, experimental verification and synthesis. The social component of the OMDB enables and stimulates the formation of virtual research groups, fostering cross-institutional interactions worldwide.

Key Papers:
  1. Organic materials database: An open-access online database for data mining.
    Borysov, Stanislav S., R. Matthias Geilhufe, and Alexander V. Balatsky,
    PloS one 12.2 (2017): e0171501
  2. Online search tool for graphical patterns in electronic band structures.
    Stanislav S. Borysov, Bart Olsthoorn, M. Berk Gedik, R. Matthias Geilhufe, Alexander V. Balatsky
    Npj Computational Materials 4.1 (2018): 46.
    https://arxiv.org/abs/1710.11611
  3. Novel Organic High-$ T_\mathrm {c} $ Superconductors: Data Mining using Density of States Similarity Search.
    Geilhufe, R. M., Borysov, S. S., Kalpakchi, D., & Balatsky, A. V. (2017).
    Phys. Rev. Materials 2, 024802
    https://arxiv.org/abs/1709.03151