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Topical Session at the DPG Spring meeting: Data ... (No replies)

Tilmann Hickel
3 months ago
Tilmann Hickel 3 months ago

Dear Psi-k colleagues,

I would like to make you aware of the Topical Session „Data driven materials science“ during the Spring Meeting of the German Physical Society (DPG), taking place March 15-20, 2020, in Dresden, Germany.  [ https://dresden20.dpg-tagungen.de/ ]

If you are performing simulations in this emerging field, you are most welcome to contribute your abstract. After choosing the MM devision on the submission page webpage [ https://www.dpg-tagung.de/dd20/submission.html ], you can find the session at the bottom of the list "Themenbereiche". The deadline is December 1, 2019.

The session is extending a symposium organized by Jörg Neugebauer, Mattias Scheffler and Kurt Kremer on "Big data driven materials science (SYBD)", which is on invitation only. The present session belongs to the MM (Metals and Materials) division and is therefore more focused on structure-composition-property relationships in materials science. Please see the abstract below for details.

Confirmed invited talks in this session will be given by Tilmann Beck (TU Kaiserslautern, Ni-based superalloys), Cecilie Hebert (EPFL Lausanne, Machine Learning in analytical TEM), Jan Janssen (MPIE Düsseldorf, Automated phase diagrams), Marcus J. Neuer (BFI Düsseldorf, Industry 4.0 for real-world applications), Stefan Sandfeld (TU Freiberg, Machine Learning-Based Classification of Dislocation).

On behalf of the organizers Erik Bitzek (Uni Erlangen-Nürnberg), Ralf Drautz (Ruhr-Uni Bochum), and myself, I am looking forward to meet you in Dresden,

Tilmann Hickel

 

Abstract: This session covers innovative high-throughput and materials-informatics approaches for the discovery, description and design of materials. The contributions should address recent developments in the fields of data mining, machine learning, and artificial intelligence for the identification of structure-composition-property relationships in the highly diverse, but often sparse materials data space. Contributions from experiment such as diffraction and various tomography techniques, materio-graphic feature identification, as well as simulation results from the atomistic up to the continuum level are foreseen. A particular focus will be on the consideration of extended materials defects (grain boundaries, stacking faults, dislocation cores) and microstructures. Furthermore, submissions of contributions on accumulating, analyzing, interpreting, storing, and sharing fundamental knowledge about materials is solicited. Contributions may range, and preferably bridge, from physics-based materials understanding to data-driven and application-oriented development.

 




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Ab initio (from electronic structure) calculation of complex processes in materials