BiGmax Summer School 2021 (virtual)
Harnessing big data in materials science from theory to experiment
September 13 - 17, 2021 (held as an online event only)
Our abilities to produce, store, and process huge amounts of information have exploded in the past decades. In parallel, the progress in advanced statistical analysis, machine learning, and artificial intelligence revolutionizes our ways of thinking about data in almost every field. In particular, these new methods aim at discovering and extracting quantitative relations from data directly, without resorting to specific theoretical models or human insight. In materials science, however, novel data-centered approaches are still less established than the traditional theoretical framework, that aims at “explaining” experimental observations by a variety of models at different length and time scales, and allows for quantitative predictions from these models directly, or via computer simulations.
To meet the challenges of the ever-growing amount of data in materials, and to use the opportunities that come with it, future materials research will need to integrate data-oriented approaches with the state-of-the-art domain knowledge. Yet, neither the current materials-science education nor the numerous available tutorials on data methods alone prepare the next generation of materials scientist to achieve this goal.
The aim of this school is to address recent advancements in structuring, analyzing, and harvesting big data in materials science. The school focuses on FAIR data representation of computational and experimental data, the development, implementation and application of machine-learning tools, and the deployment of novel mathematical approaches for data mining and diagnostics. An additional emphasis of the school will be laid on unified approaches in representing big data sets and machine-learning algorithms, spanning across the different disciplines from theory to experiment and within the diverse experimental and theoretical approaches.
The school focuses on combining lectures of renowned experts with hands-on tutorials predominantly targeted towards PhD students and early career researchers.
- Ankit Agrawal (Northwestern University, USA)
- Baptiste Gault (Max-Planck-Institut für Eisenforschung, Germany / Imperial College London, UK)
- Nicole Jung (Karlsruhe Institute of Technology, Germany)
- Christoph Koch (Humboldt-Universität zu Berlin, Germany)
- Sergei V. Kalinin (Oak Ridge National Laboratory, USA)
- Francisco De La Peña (Université de Lille, France)
- Milica Todorović (Aalto University, Finland)