Machine learning modalities for materials science

Machine learning methods are revolutionizing the way we approach materials design, making an impact in each, yet, it is rare that they fully exploit information and data from different modalities and sources. The aim of this workshop is thus two-fold:

• Young researchers will have the opportunity to grow solid foundations and a complete overview of cutting-edge machine-learning methods that enable the community to tackle outstanding challenges across diverse domains in materials design and discovery.
• Attendees will have the chance to discuss and identify routes on how to best combine information of different nature towards a unified vision (and solution) of the material design and discovery problem. To this end, during the workshop part, invited and contributed speakers, and panel discussions will take place, with a focus on multi-modal, multi-objective, and multi-fidelity machine learning methods in materials science.

The participation in the workshop is free of charge. The programme is available here.

A workshop  organized by Sašo Džeroski, Jožef Stefan Institute, SI; Stefano de Gironcoli, SISSA, IT; Patrick Rinke, Aalto University, FI; Kevin Rossi, TU Delft, NL; Sintija Stevanoska, Jožef Stefan Institute, SI; Milica Todorovic, University of Turku, FI.

Supported by: AIJ, TAILOR, MaX, CECAM, PSI-K and SMASH.

Official webpage: https://ml4ms.ijs.si/