Overview

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Tapping into world-leading expertise and facilities in Germany and Canada, the German-Canadian Materials Acceleration Centre offers one of the top innovative and agile ecosystems in energy materials science. The underlying research capabilities to enable robotic materials acceleration platforms (MAPs) spans from fundamental materials theory and modelling, interfacial electrochemistry, AI-enabled materials (inverse) design, high-throughput computational and experimentation methods, design-to-device workflow, data science and analytics, and advanced characterization tools and methods. GCMAC aligns its R&D strategy with the global transition towards a de-fossilized, decentralized, efficient, and economically viable energy infrastructure. 

WP1: THEORY AND MULTISCALE MODELING
The full-cycle approach in theory and computation

WP2: AUTONOMOUS ROBOTIC PLATFORMS
Experimental workflow development and systems integration of equipment for fabrication and characterization of energy materials

WP3: AI-DRIVEN DESIGN AND ADVANCED SIMULATIONS
Developing and deploying AI and ML tools for challenges related to the prediction of properties of energy materials and devices, accelerated computational design of materials and accelerated atomistic simulations and multiscale modelling workflows 

WP4: CHARACTERIZATION AND FABRICATION TECHNOLOGIES
Deploying analytical techniques involving microscopic, spectroscopic, electrochemical methods; operand spectroscopy and imaging; and performance-lifetime testing of materials, components and devices. 

WP5: AI-BASED DATA HANDLING AND WORKFLOW OPTIMIZATION
Addressing inefficiencies in the quality, supply, and management of materials R&D data, real-time data accessibility and traceability, and development of effective AI-based pipelines