Regensburg 2019 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 37: Topical session (Symposium MM): Big Data Analytics in Materials Science

MM 37.8: Talk

Thursday, April 4, 2019, 17:45–18:00, H43

Electronic density-of-states fingerprints for finding similar materials — •Martin Kuban, Santiago Rigamonti, Markus Scheidgen, and Claudia Draxl — Humboldt-Universität zu Berlin

The recent development of large databases for computational materials science, like NOMAD [1], allows researchers to reuse data that was generated for different purposes. In this work, we make use of the data contained in NOMAD to find materials with similar properties. Similarity can be evaluated and quantified by comparing specialized representations of the materials properties, so-called fingerprints. We design a family of fingerprints derived from the electronic density-of-states (DOS), consisting of vectorial representations obtained from non-uniform scalings of the DOS. In contrast to previous works [2], our approach allows us to set the focus of searches for similar materials on special features of the DOS, as for instance the band gap, or the amount of states close to the Fermi level. We present examples for several materials ranging from metals to insulators. To demonstrate the usefulness and applicability of our approach, we have devised a recommender system for the NOMAD Encyclopedia.

[1] C. Draxl and M. Scheffler, MRS Bulletin, 43, 676, (2018).

[2] O. Isayev et al., Chermistry of Materials 27, 735, (2015).