GCMAC Summer School 2023
The GC-MAC Summer School 2023 will be held in Karlsruhe, Germany, on 18-22 September 2023. With the theme “MAPs for energy materials – Experiments, simulations, and machine learning,” the school will cover multiple aspects of materials acceleration platforms (MAPs) for energy materials. This includes experimental aspects of automated synthesis and characterization, integrating simulation methods in materials design, and machine learning methods for MAPs. Experts from different fields will hold lectures and provide hands-on tutorials on their subjects. Additionally, we will visit the battery MAP at the Helmholtz Institute Ulm and plan social and networking activities and a poster session. The participation fee of 370 € covers organization, local support, lunch and drinks during the day, the excursion to Ulm, a visit to Heidelberg, and the social dinner. Transport to and from Karlsruhe, as well as accommodation, needs to be organized and paid for by the participants individually. The organizing team is happy to assist and answer any questions – don’t hesitate to contact Tobias Schlöder (firstname.lastname@example.org) or Pascal Friederich (email@example.com) for more information.
More details will follow soon! Please note interested candidates are invited to submit an application form to attend through the registration link. If/once approved – a link for final information and payment will be provided
Impactful Materials Science
Autonomous discovery, development and deployment using AI, data analytics, robotics, and high-performance computing
Push key technologies, disseminate know-how and train next-generation of energy-materials-intelligence experts
Connecting infrastructure in leading German-Canadian partner organizations and associated universities
Accelerating Discovery of Energy Materials
German-Canadian materials acceleration center (GC-MAC) amalgamates research communities in Germany and Canada on a topic of utmost strategic importance: harnessing artificial intelligence and advanced machine learning to accelerate the discovery, design, device integration, and demonstration of materials for sustainable energy technologies.
Supporting the growth of materials acceleration platforms (MAPs), the Centre will assist in aligning approaches and directions; promote common methods, standards, and collaborative actions; and establish a new regimen for the training of scientists and engineers who will lead future developments at the interface of materials science, energy technology and information science.
At a Glance
5 materials challenges
Key challenges in electrocatalysis, ionic media, interfaces and electrodes, porous transport media, and materials to devices.
20+ associated experts
World renown experts in electrocatalyst materials, ionic media, electrode and cell fabrication, porous media, and electrochemical energy devices.
5 technical work packages
- Theory and multiscale modeling
- Autonomous robotics platforms (MAPs)
- AI-driven design and advanced simulations
- Characterization and fabrication technologies
- AI-based data handling and workflow optimization