Project description
AI-guided catalyst materials for high-performance zinc-air batteries
The slow speed of the chemical reactions inside safer alternative batter systems impedes moving away from lithium-based batteries. While zinc-air batteries are cheap, non-flammable and recyclable, they lose significant power because oxygen molecules cannot efficiently interact with the battery electrodes. Supported by the Marie Skłodowska-Curie Actions programme, the PEACE-ZABs project aims to apply machine learning to accelerate the discovery of complex atomic structures known as high-entropy perovskite oxides. This approach will enable researchers to instantly predict which atomic distributions will maximise energy density and reduce long-term degradation. Rather than relying on traditional, slow trial-and-error laboratory testing, PEACE-ZABs’ approach fast-tracks AI-optimised materials, moving them into the commercial production line.
Objective
Zinc–air batteries (ZABs) are emerging as a promising alternative to Li-based systems due to their high gravimetric energy density and the abundance, low cost, and recyclability of zinc, a non-critical raw material. In addition, ZABs employ safe, non-toxic, and non-flammable aqueous electrolytes, enhancing further their competitiveness. However, their practical deployment remains hindered by intrinsic physicochemical limitations, the most critical one arises from the sluggish oxygen electrocatalysis, leading to high overpotentials in both oxygen reactions (OER/ORR). High-entropy perovskite oxides (HEPOs) are promising electrocatalysts for ZABs due to their tunable high-entropy structure, which can enhance OER/ORR activity and improve electrochemical stability compared to conventional oxides. Their complex atomic distribution is expected to enhance the stability in acidic electrolytes, allowing higher cell potentials (up to 2.55 V) and increasing the energy density while reducing long-term degradation. The PEACE-ZAB project aims to accelerate the discovery of lanthanum-based HEPOs for ZABs using machine learning (ML) on high-throughput experimental data. The project will (i) explore HEPO compositional spaces with reduced critical raw material content, (ii) elucidate the relationships between composition, structure, and electrochemical performance (OER/ORR activity and stability in acidic/alkaline electrolytes) through ML-assisted analysis, and (iii) transfer the most promising candidates to industrial production with startup Nano4Energy. The project will deepen the candidate’s expertise in electrochemical devices and machine learning - both key enabling technologies for Horizon Europe program - while leveraging his background in high-throughput experimentation for energy materials. The project will also re-enforcing the candidate’s transferrable skills and technology transfer competence as part of the EIC Innoenergy community and the clean energy R&D&I sector.
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.
- engineering and technology environmental engineering energy and fuels renewable energy
- natural sciences chemical sciences catalysis electrocatalysis
- natural sciences chemical sciences inorganic chemistry transition metals
- engineering and technology environmental engineering mining and mineral processing
- natural sciences computer and information sciences artificial intelligence machine learning
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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.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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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.
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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
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Call for proposal
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(opens in new window) HORIZON-MSCA-2025-PF
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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.
08930 Sant Adria De Besos
Spain
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