CECAM Online Workshop (June 9, 17 & 23, 2021)

AI for Materials Science: Mining and Learning Interpretable, Explainable, and Generalizable Models from Data.

Organizers: Luca Ghiringhelli, Jilles Vreeken, and Matthias Scheffler

In artificial intelligence (AI), it is in general challenging to provide a detailed explanation on how a trained model arrives at its prediction. Thus, usually, we are left with a black-box, which from a scientific standpoint is not satisfactory. Even though numerous methods have been recently proposed to interpret AI models, somewhat surprisingly, interpretability in AI is far from being a consensual concept, with diverse and sometimes contrasting motivations for it. Reasonable candidate properties of interpretable models could be model transparency (i.e. how does the model work?) and post hoc explanations (i.e., what else can the model tell me?).

The idea of the workshop is to bring together materials scientists who have been already applying AI techniques in their field and AI experts who are particularly interested in interpretability, explainability, and generalizability issues related to the trained AI models. The AI experts are not expected to be already familiar about materials science. The core task will be to understand if and how physics, and in particular materials science, is in some sense special with respect to interpretability, explainability, and generalizability and if therefore needs special, possibly yet to be developed concepts and tools.

Program and other details: https://nomad-coe.eu/cecam_online_workshop

To register and obtain the zoom link, contact Luca Ghiringhelli ([email protected])