Project description
Material design for solid oxide fuel cells using machine learning
Microscale thin-film-based solid oxide fuel cells (μSOFCs) are considered a promising future technology for portable power applications. This emerging alternative boasts high efficiency, fuel flexibility and high power densities. Improved oxygen transport properties and resistance to the high operating temperatures render lanthanum-based oxide materials suitable for use in the cathodes of such type of fuel cells. Funded by the Marie Skłodowska-Curie Actions programme, the SmartOptoelectronics project will test machine learning methods to study the relationship between the structural properties and the electrochemical performance of perovskite lanthanum-based oxides based on high-throughput experimental data. By exploring the chemical space of lanthanum-based oxides, the project will design materials with enhanced performances for use in μSOFCs.
Objective
Microscale thin-film-based solid oxide fuel cells (μSOFCs) are an emerging alternative for portable power supply due to their high efficiency, fuel flexibility and high volumetric and specific power densities. Promising cathode materials for μSOFCs are perovskite lanthanum-based oxide materials which have improved oxygen transport properties and resistance to the high operating temperatures. However, the physicochemical factors influencing the performance of these materials are yet to be well understood. The SmartOptoelectronics project will develop machine learning (ML) methods to establish trends between the structural properties and the electrochemical performance of perovskite lanthanum-based oxides based on high-throughput experimental data. These techniques will be used to explore the chemical space of lanthanum-based oxides with the goal of undestanding and designing lanthanum-based materials with enhanced performances for μSOFC applications. Machine learning methods will be validated in three main steps: (1) deriving structure-property relationships in lanthanum-based oxides from spectroelectrochemical data of combinatorial ternary and quaternary maps; (2) demonstrating new lanthanum-based oxides with enhanced electrochemical properties and performance; (3) optimising the operation of devices based on top-performing materials with operando monitoring of spectroelectrochemical properties. The project will have a high impact on the work programme and on the candidate’s skills and future prospects by developping an expertise in machine learning and large scale clean energy conversion devices, which are Key Enabling Technologies in Horizon Europe and complement her background in spectroelectrochmistry of multi-redox catalytic materials. The project will also re-enforcing the candidate’s transferrable skills and technology transfer competence as part of the KIC Innoenergy community and the clean energy R&D&I sector.
Fields of science
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencesartificial intelligencemachine learning
- engineering and technologyenvironmental engineeringenergy and fuelsfuel cells
- engineering and technologyenvironmental engineeringenergy and fuelsenergy conversion
Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinator
08930 Sant Adria De Besos
Spain