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Big-Data-Driven Materials Science: Critical Role of the Descriptor

Wednesday, 31 January 2018, 16:30-18:00
KIT, Campus Nord
Institute of Nanotechnology
Bldg. 640 Seminar room 0-167
Hermann-von-Helmholtz-Platz 1
76344 Eggenstein-Leopoldshafen

Talk given by

Dr. Luca M. Ghiringhelli
Fritz-Haber-Institut der Max-Planck-Gesellschaft
Berlin, Germany



The number of possible materials is practically infinite, while only few hundred thousands of (inorganic) materials are known to exist and for few of them even basic properties are systematically known. In order to speed up the identification and design of new and novel optimal materials for a desired property or process, strategies for quick and well-guided exploration of the materials space are highly needed. A desirable strategy would be to start from a large body of experimental or theoretical data, and by means of “big-data-analytics” methods, to identify yet unseen patterns or structures in the data. This leads to the identification of maps (or charts) of materials where different regions correspond to materials with different properties. The main challenge on building such maps is to find the appropriate descriptive parameters (called descriptors) that define these regions of interest.

Here, I will present methods for the machine-aided identification of descriptors and materials maps applied to the metal/insulator classification, the prediction of novel 2D topological insulators, and the construction of a tolerance factor for the stability of perovskites.

I will also describe the infrastructure to perform such analyses online, via the "Big-data-analytics toolkit" within the framework of the Novel-Materials-Discovery (NOMAD) Laboratory


(Picture courtesy of Dr.  Luca M. Ghiringhelli)

Dr. Luca Ghiringhelli

Fritz Haber Institute of the Max Planck Society
Fitz-Haber-Institut, Berlin
Prof. Dr. Manfred Kappes
Institute of Nanotechnology (INT)
Karlsruhe Institute of Technology (KIT)
Mail:manfred kappesNel4∂kit edu
Interested / Everyone