SYMPOSIUM on Managing And Exploiting The Raw Material of The 21st Century

Symposium at the 2018 Joint meeting of the DPG and EPS Condensed Matter Divisions
Germany, Berlin, 11 March - 16 March 2018




With more than 5,000 expected participants the DPG Spring Meeting is the largest European physics conference (the second largest physics conference worldwide) covering all aspects of condensed matter and chemical physics, materials science, surface science, as well as polymer and biophysics, and more. For a few years now the conference language has been English.

You are invited to attend the 2018 Joint meeting of the DPG and EPS Condensed Matter Divisions from March 11-16, 2018 in Berlin and in particular the symposium

Big data in Materials Science – Managing and exploiting the raw material of the 21st century

organized by Claudia Draxl (Humboldt-Universität zu Berlin) and Peter Fratzl (Max-Planck-Institut für Kolloid- und Grenzflächenforschung, Golm).

This is an official topical session at the DPG spring meeting of the Condensed Matter Section which is this year held together with the EPS (you can find a list of the topical sessions here). There will be invited talks, contributed talks, and a poster session. We are grateful that the symposium is hosted by the Division of Metal- and Materials Physics of the DPG.

Invited Speakers at the symposium include:

  • Stefano Curtarolo, Duke University, USA (Challenges of running large data collections in materials science)
  • Manuel Guizar-Sicairos, Paul Scherrer Institut, Villigen, Switzerland (Tensor tomography of anistropic and inhomogeneous materials)
  • Cecile Hebert, EPFL Lausanne, Switzerland (Data diagnostics in electron microscopy)
  • Christoph Schweizer, Frauenhofer IWM, Germany (Digital representation of materials and microstructure-property-relationships; ontology and metadata)
  • Jilles Vreeken, MPI Saarbrücken, Germany (Discovering Interpretable Patterns, Correlations, and Causality)
  • Jan Vybíral, Czech Technical Univeristy, Prague, Czech Republic (Compressed sensing for data analytics in materials science)

Endorsed by BigMax,the Max Planck research network on big-data-driven materials science: