SOURCE-LINKED INTELLIGENCE

FULLy integrated, autonomous & chemistry agnostic Materials Acceleration Platform for sustainable batteries

CORDIS · observation · Publication date unknown

and expedites the discovery of new materials and interfaces. This pivotal initiative focuses on automating laboratory operations and conducting fast, high-throughput experiments. It integrates AI and machine learning-accelerated multi-scale and multi-physics modeling, supporting intelligent decision-making. FULL-MAP's comprehensive, modular approach encompasses the inverse design of materials, autonomous orchestrated production via both traditional and novel synthesis routes, and extensive high-throughput characterization methods. These methods span ex-situ, in-situ, operando, on-line, and post-mortem analyses at various levels, from material to cell assembly and testing. It simulates the entire battery development process, from material design to battery testing, considering environmental and economic factors. By integrating computational and experimental methods with AI, Big Data, Autonomous Synthesis, and High-Throughput Testing, FULL-MAP aims to fast-track the development and deployment of next-generation materials and batteries, significantly advancing sustainable battery techno

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recordType

award

status

SIGNED

region

EU

value

19949437.4

unit

EUR