Over the past decades there has been an an enormous increase of computational power and a rapid development of experimental techniques. Both developments, together with the great advancements of data storage capacities, have initiated the application of methods taken from computer and data science into the research of functional quantum materials and quantum many-body physics. For example, interpretable and computationally-efficient machine learning models are able to capture the structure-property relationship in materials science opening the path towards an efficient computer based materials design.

In supervised learning, large data sets, e.g., of ab initio calculations, provide the necessary training examples. The trained models facilitate high-throughput screening of materials by reducing the search space. Additionally, the models enable dynamic simulation on longer timescales than traditionally feasible. Unsupervised clustering approaches using structural similarity metrics allow for a new way of exploring the large chemical space. In case of the many body problem, machine learning architectures provide versatile wavefunctions that lead to accurate results and prove to be more flexible than traditional methods.

Work in the group has focused on developing data mining and machine learning techniques to investigate and analyse the calculated properties of organic materials. This is closely linked with the development of the Organic Materials Database (OMDB).

• OMDB: The Organic Materials Database (OMDB) contains DFT data for over 25,000 organic crystals that have been synthesised and provides the data set for the group’s material informatics work.

• Magnetism: Magnetic properties are of interest from both fundamental and application perspectives; atomistic spin dynamics protocols are being used to extend the DFT data of the OMDB.

• Machine learning: machine learning techniques are being developed to predict potentially interesting materials based on the data contained in the OMDB.

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 more than 40,000 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

4. Shifting computational boundaries for complex organic materials, Geilhufe, R.M., Olsthoorn, B. & Balatsky, A.V. (2021).
   Nat. Phys. (2021)