Project description
AI-driven design of high-performance energy materials
Solid-state electrolytes are crucial for sustainable energy storage, yet their development is hindered by low efficiency and high testing costs. Supported by the Marie Skłodowska-Curie Actions programme, the LIMG project aims to launch a Loop-Locked Intelligent Material Generation platform, an AI-driven system designed to accelerate the discovery of high-performance materials. By using specialised machine learning to simulate lithium movement with high fidelity, the project aims to bypass slow, expensive traditional computations. Additional objectives include building a massive, reusable material database and employing generative AI to design new electrolytes with specific properties. The project’s goal is to revolutionise how we create energy materials, speeding up the transition to low-carbon technologies while training the next generation of leaders in AI-driven science.
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.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences computer and information sciences databases
- natural sciences computer and information sciences artificial intelligence generative artificial intelligence
- natural sciences computer and information sciences artificial intelligence machine learning
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2025-PF
See all projects funded under this callCoordinator
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.
4365 ESCH-SUR-ALZETTE
Luxembourg
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.