SOURCE-LINKED INTELLIGENCE

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

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

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recordType

award

status

SIGNED

region

EU

value

200400

unit

EUR