Abstract
(Englisch)
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Battery technology emerges as a key solution for cutting carbon dioxide emissions across transportation, energy, and industrial sectors. Nonetheless, traditional research methods for developing new battery materials have typically depended on an Edisonian approach, characterised by trial and error, where each phase in the discovery value chain is sequentially reliant on the successful execution of preceding steps. Development and optimization of novel batteries is a process that spanned around a decade. To face this challenge, it is necessary to accelerate the discovery and optimization of next-generation batteries through the development of materials and interface acceleration platforms. The FULL-MAP project aims to revolutionize battery innovation by developing a materials acceleration platform that amplifies human capabilities 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 nextgeneration materials and batteries, significantly advancing sustainable battery technology.
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