Materials science is entering an era where the growth of data from experiments and simulations is expanding beyond a level that is addressable by established scientific methods. The so-called “4 V challenge” – concerning 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), and Veracity (uncertainty of quality) is clearly becoming eminent. Issues are, for example, an early discrimination between valuable and irrelevant experimental data, understanding errors in both experiment and theory, and assigning error bars and trust levels to density-functional theory high-throughput screening results, just to name a few. Most importantly, however, is that Big Data of materials science provide a significant chance for new insight and knowledge gain when fully exploiting its information by artificial intelligence concepts and methods. All the above aspects – from data processing to exploiting the potentials of data-driven materials science – require new and dedicated approaches.
This school is predominantly targeted towards PhD students and young postdocs. It will address important background and recent advances in data-driven materials science. The topics will cover a wide spectrum to demonstrate the challenges and potential that research data offer. This will include the FAIR principles of scientific data, including hardware aspects; introduction and frontiers of artificial intelligence; interpretability and causality in machine learning; various data-mining tools and mathematical concepts behind; data diagnostics; pattern discovery; real-time data processing of emerging experimental setups; metadata in computational and experimental materials science; and more.
The school is organized by Gerhard Dehm (MPI for Iron Research), Claudia Draxl (Humboldt-Universität zu Berlin), Matthias Scheffler (Fritz Haber Institute), and Jilles Vreeken (MPI for Informatics, Saarbrücken University) and will take place September 9-13, 2019 at the Hotel Cap Roig overlooking the Mediterranean Sea.
You can find more information on BiGmax, the Max Planck Research Network on big-data-driven materials science, here: https://www.bigmax.mpg.de/.
Topics and Speakers
Invited speakers include:
- Gerbrand Ceder (University of California, Berkeley, Lawrence Berkeley National Laboratory, USA)
- Karsten Jacobsen (Danish Technical University, Denmark)
- Kristin Persson (Lawrence Berkeley National Laboratory, USA)
- Dierk Raabe (Max-Planck-Institut für Eisenforschung GmbH, Düsseldorf, Germany
- Markus Rampp (Max Planck Computing and Data Facility, Garching; Germany)
- Rampi Ramprasad* (Georgia Tech, USA)
- Bernhard Schoelkopf* (Max Planck Institute for Intelligent Systems, Tübingen, Germany)
- Gerhard Weikum* (Max Planck Institute for Informatics in Saarbrücken, Germany)
* to be confirmed
(more names will be added soon)
Each lecturer will contribute a 60-minute pedagogical presentation and additionally a 20-minute “hot topic” on recent research.