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LIMG: Artificial Intelligence-Driven High-Fidelity Inverse Design of Solid-State Electrolytes

Objective

Solid-state electrolytes (SSEs) promise to revolutionize energy storage and carbon-neutral mobility industries, but their low ionic conductivity remains a barrier. Discovery of high-performance SSEs is hindered by a lack of high-quality lithium-diffusion data, unclear structure-property understanding, and the high cost of traditional computational screening. This project aims to build a Loop-Locked Intelligent Material Generation (LIMG) platform by coupling a pre-trained machine learning force field (MLFF) called SO3LR-SSE with advanced generative models. SO3LR-SSE explicitly incorporates non-local and many-body interactions to improve the fidelity of dynamic SSE databases and the transferability across diverse material systems. The project includes four main work packages (WPs 1-4, LIMG platform) plus one for data management, career development, and dissemination. WP1 will develop a high-fidelity, transferable MLFF for SSEs to speed up Li-diffusion dynamics simulations, forming the basis for generating a comprehensive SSE database of ionic conductivities in WP2. WP3 will use ML and the database to accurately predict ionic conductivity and elucidate structure-property relationships. WP4 will focus on inverse SSEs design with clear physical interpretability based on generative models. LIMG is expected to generate a large, reusable dataset and rapidly identify novel candidates across global chemical spaces for experimental follow-up. A planned secondment at TU Berlin will support MLFF development in a collaborative environment. For the fellow, this project will significantly strengthen skills in advanced AI algorithm development, materials design, and interdisciplinary collaboration, strengthening prospects for leadership in AI-driven materials research. LIMG aspires to be widely expanded to material systems, advancing innovative, interpretable, high-fidelity design of new energy materials and accelerating deployment of low-carbon energy storage technologies.

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Topic(s)

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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-2025-PF

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Coordinator

UNIVERSITE DU LUXEMBOURG
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.

€ 200 400,00
Address
2 PLACE DE L'UNIVERSITE
4365 ESCH-SUR-ALZETTE
Luxembourg

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Region
Luxembourg Luxembourg Luxembourg
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

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