Events

We are excited to invite you to the upcoming workshop “Machine Learning with Computational Materials Science Data for Modeling Nanocrystal Catalysts” at Fraunhofer IWM in Freiburg, Germany, under the COST Innovators Grant IG18234. This workshop aims to bring together leading experts from academia and industry to discuss the future of machine learning methods and the infrastructure needed for predicting catalytic properties, particularly in relation to industrial applications and hydrogen production.

In terms of both usage and underlying technology, modern data management concepts differ significantly from traditional approaches such as spreadsheet-based storage or conventional databases. Often, data is scattered across silos, stored in inconsistent formats, or even lost over the course of a project. The result: valuable knowledge remains untapped, traceability requires extensive manual effort, and data-driven optimizations or AI applications fail due to poor data quality. Focusing on material and process data, this training course teaches you how to make such data findable, linkable, and reusable – using FAIR data principles, data spaces, ontologies, and knowledge graphs, all applied in a hands-on way to material, experimental, and process data. This increases transparency and accelerates analytics and decision-making processes.