Overview

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 modeling, 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.

Topics

Materials challenge

electrochemical energy systems with escalating or hierarchical complexity.

Advance method and tool development

AI and different advanced machine learning (ML) methods and apply them to specific materials science problems.

Accelerate discovery, design, and scale-up of energy materials

to achieve rapid identification of superior material performance within a given design space, e.g., with respect to composition, atomic structure, or microstructure. GCMAC deploy new analytical techniques sufficient to autonomously derive material and/or device performance.

Accelerated research on candidate materials

synthesis, fabrication, characterization and testing, and integration by leveraging the assets associated with AI-driven, smart robotic systems known as Materials Acceleration Platforms (MAPs)

Work Packages:

WP1

THEORY AND MULTISCALE MODELING

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 modeling 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