AI4MI is an online course aiming to bridge the gap between materials researchers and artificial intelligence. It is built around four case studies which guide the user through solving practical materials problems using AI. Each case study explains the problem, the theoretical details of the models and how to use them, as well as interpretation using explainable AI. In this way it aims to increase the adaption of AI techniques for those used to working with physical models, as well as provide inspiration for researchers and educational users to include these generic AI techniques in their own projects. Recently we have started a new Horizon project AID4GREENEST which will add further content as well as build a new open data repository for steel data including microscopy images and data (OM, (B)SE, EBSD) and creep data.

The AID4GREENEST project aims to develop the development of green steel using AI. The project will focus on the extraction of creep and EBSD data, as well as material properties and processing parameters using self- and semi-supervised learning, as well as generative AI. More classical machine learning and deep learning techniques will also be further developed. An open data repository will also be developed to make the data public, powered by AI tools to automatically curate the data. AI4MI will be used as a supporting platform to share further results as well as provide examples on how to use the dataset. If you want to stay informed or contribute data, don’t hesitate to contact us!