Driven by the availability of large data sets and the application of artificial intelligence algorithms, materials science is currently undergoing a dramatic transformation. In particular, machine learning approaches are opening up new possibilities to model, understand, design and discover new materials. In this summer school, geared towards doctoral and advanced master students, participants will gain an understanding of various machine learning methods and learn about their application to hard and soft materials. Introductory lectures will be complemented with hands-on exercises as well as research talks about the current state of the art.
There is no registration fee but registration is compulsory. Registration deadline is 26 April 2022. It is planned that the school will take place on site. However, the school may be switched online if required due to the COVID situation.
MSc and PhD students have the opportunity to apply for travel support.
Main Lectures
Topic | Lecturer |
Mathematical introduction | Philipp Grohs (University of Vienna) |
Image analysis | Thomas Pock (TU Graz) |
Machine Learning for electronic structure | James Spencer & David Pfau (Deep Mind) |
Machine Learning potentials | Philipp Marquetand (University of Vienna) |
Statistical sampling | Peter Wirnsberger (Deep Mind) |
Free energies and enhanced sampling | Luigi Bonati (ETH Zurich & IIT Genova) |
Materials properties prediction | Taylor D. Sparks (University of Utah) |
Research Talks
- Bingqing Chen (ISTA)
- Roberto Covino (FIAS)
- Marjolein Dijkstra (University of Utrecht)
- Andrew Fergusson (University of Chicago)
- Reinhard Maurer (University of Warwick)
- Milica Todorovic (University of Turku)
Case studies
On Friday, 15 July, we will welcome Machine Learning experts who translated their knowledge into the private sector.
Schedule
The full schedule can be found here.
Organization
The ESI-DCAFM-TACO-VDSP Summer School 2022 "Machine Learning for Materials Hard and Soft" is organized by Christoph Dellago (University of Vienna), Ulrike Diebold (TU Wien), Leticia González (University of Vienna) and Jani Kotakoski (University of Vienna). Financial support by the FWF (doc.funds HiDHyS, SFB TACO), ESI and the VDSP is highly appreciated.
Further information
Please find all further details as well as the registration form on the event's website.
