News

31/08/2017
The electronica blog recently posted an article about BigMax here.

25/07/2017
The Max Planck Society released a press note about BigMax. The full press release can be found here.

Upcoming

April 15-18, 2019
BiGmax Workshop 2019 on Big-Data-Driven Materials Science at mpipks Dresden.
Organizers: Tristan Bereau, Jan M. Rost

April 10-13, 2018
BiGmax Workshop 2018 on Big-Data-Driven Materials Science at Kloster Irsee.
Organizers: Peter Benner, Jörg Neugebauer

March 11-16, 2018
Spring meeting of the German Physical Society: DPG-condensed matter jointly with the EPS, Berlin

SYMPOSIUM at Fachverband Metall- und Materialphysik (MM): Managing And Exploiting The Raw Material of The 21st Century. Organizers: C. Draxl and P. Fratzl

Fachverbandsübergreifendes” Symposium on Data-Driven Methods in Molecular Simulations of Soft-Matter Systems, Organizers: D. Frenkel, Ch. Dellago, K. Kremer.


Contact

Scientific Coordinators

Matthias Scheffler
Fritz Haber Institute of the Max Planck Society, Berlin
Phone: +49 30 8413 4711
E-mail: scheffler@fhi-berlin.mpg.de

Peter Benner
Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg
Phone: +49 391 6110 450
E-mail: benner@mpi-magdeburg.mpg.de

Coordination Assistant

Hanna Krauter
Fritz Haber Institute of the Max Planck Society, Berlin
Phone: +49 30 8413 4720
E-mail: krauter@fhi-berlin.mpg.de

BigMax, the Max Planck Research Network on Big-Data-Driven Materials Science

Materials science is entering an era where the growth of data from experiments and calculations is expanding beyond a level that is properly processable by established scientific methods. Dealing with this big data is not just a technical challenge but much more it is a great chance. Big-data analytics will revolutionize new material discovery and will make the sucessful search of structure-property relationships among multiple lengthscales and timescales possible.
Thus far, the search for new materials for new applications was limited to educated guesses mostly based on selective experiments. By tackling this big data challenge with high-speed computing  extremely large, disparate databases and large-scale computations have to be dealt with. But recent advances in data mining will allow pattern recognition and pattern prediction in an unprecedented way. The outcome of big-data-driven materials science approaches will then impact the way experiments and data analyses are done.

Big Data

Materials science is entering an era where the growth of data from experiments and calculations is expanding beyond a level that is properly processable by established scientific methods. Dealing with this big data is not just a technical challenge but much more it is a great chance. Big-data analytics will revolutionize new material discovery and will make the sucessful search of structure-property relationships among multiple lengthscales and timescales possible.
Thus far, the search for new materials for new applications was limited to educated guesses mostly based on selective experiments. By tackling this big data challenge with high-speed computing  extremely large, disparate databases and large-scale computations have to be dealt with. But recent advances in data mining will allow pattern recognition and pattern prediction in an unprecedented way. The outcome of big-data-driven materials science approaches will then impact the way experiments and data analyses are done.

4 V Challenge


At several Max Planck Institutes of the Section for Chemistry, Physics, and Technology (CPTS), the so-called “4 V Challenge” is becoming eminent:

  • Volume: the amount of data,
  • Variety: the heterogeneity of form and meaning of data,
  • Velocity: the rate at which data may change or new data arrive,
  • Veracity: uncertainty of quality.

The last item has been identified as being of particular importance for the big-data issues of the CPTS. To name just a few examples from different research directions: an early discrimination between valuable and irrelevant data (e.g. by data diagnostics in x-ray tomography), understanding position errors and why 10-20% of atoms fail detection in atom-probe tomography, assigning error bars and trust levels to density-functional theory high-throughput screening results, are urgently needed.

Big Data in Materials Science


In addition to the “4 V Challenge”, the key motivation of this Max Planck Research Network is that completely new insight and knowledge gain can be expected if it became possible to fully exploit the information content in the already available and strongly increasing big data of materials. This exploitation requires new and dedicated technology based on approaches in statistical and machine learning, compressed sensing, and other recent technologies from mathematics, computer science, statistics and information technology.


Expertise at the Max Planck Society


The teams of the involved Institutes of the Max Planck Research Network BigMax cover a significant breadth in research areas, and they are convinced that the envisioned synergy will enable them and their MPIs to develop novel, domain-specific and property-specific methods to enter and shape the era of data-driven materials research.
The goal of BigMax is to fully exploit these scientific potentials of materials science activities of the CPTS and to raise the consortium to world leadership in data-driven materials science.

Topics in Materials Science


Popular topics in materials science are the discovery of new materials, estimation of phase diagrams, crystal structures, material properties, interatomic potentials, energy functionals, density functionals or lattice models. Other materials related deep learning applications are materials processing, automated micrograph analysis or structure-property relationships in amorphous materials.

 
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