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AI-Accelerated Design of Electrolytes for Robust Inorganic-Rich SEI in Li/Na/Mg Batteries

Project description

AI model for solvents for metal batteries

Battery technology is key for decarbonisation, but the development of high-energy lithium-metal batteries has been delayed by safety concerns related to the solid electrolyte interphase (SEI). SEIs rich in inorganic components offer improved safety and long-term stability, and weakly solvating electrolytes (WSEs) show strong potential in this area. Supported by the Marie Skłodowska-Curie Actions programme, the AI4Bat Joint Project is developing an AI model capable of predicting optimal solvents for WSEs in lithium, sodium, and magnesium metal batteries. The project will use atomic-level simulations to establish links between solvent properties and anion reduction processes in lithium batteries. Promising solvents will then be formulated into WSEs and tested in pouch cells to evaluate their performance.

Objective

Battery technology is critical to achieving global decarbonization, but the commercialization of high-energy Li-metal batteries has been stalled for decades due to safety risks and limited lifespan. These issues stem from the failure of the solid electrolyte interphase (SEI) on the Li-metal anode. Conventional carbonate-based electrolytes lead to an organic-rich SEI that is porous, ionically insulating, prone to dendrites formation, dissolution, and electron tunneling, resulting in continuous battery degradation. In contrast, an inorganic-rich SEI, formed by anion decomposition, is dense, insoluble, electron-insulating, mechanically robust, and dendrite-freeoffering a path toward safer and more durable batteries. Weakly solvating electrolytes (WSE) have emerged as a promising solution for creating such SEIs, but their development remains slow, relying on inefficient trial-and-error methods. The AI4Bat project aims to revolutionize this process by developing an AI-model capable of predicting optimal solvents to form WSEs for Li, Na, and Mg-metal batteries. Starting with Li-metal batteries, the AI-model will link solvent properties to anion reduction in the electric double layer using atomic simulations. This model will then screen new solvents from extensive databases, which will be formulated into WSEs and tested in pouch cells. The performance of these WSEs will be rigorously evaluated, focusing on the inorganic, ion-conductive, electron-insulating, mechanical properties and insolubility of the SEI, and correlated with overall battery performance. Upon successfully validating the AI-model in Li batteries, I will transfer it to Na/Mg-metal batteries to enhance their electrochemical performance through the development of robust inorganic-rich SEIs. This project will significantly accelerate the design of safer, high-performance batteries, paving the way for next-generation energy storage and conversion technologies.

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2024-PF-01

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Coordinator

KEMIJSKI INSTITUT
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 198 557,52
Address
HAJDRIHOVA 19
1000 Ljubljana
Slovenia

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Region
Slovenija Zahodna Slovenija Osrednjeslovenska
Activity type
Research Organisations
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Total cost

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