Description du projet
Concevoir des matériaux pour les piles à combustible à oxyde solide à l’aide de l’apprentissage automatique
Les piles à combustible à oxyde solide à couches minces à l’échelle microscopique (μSOFC) sont considérées comme une technologie future prometteuse pour les applications d’énergie portable. Cette alternative émergente se distingue par son haut rendement, sa flexibilité en matière de carburant et ses densités de puissance élevées. L’amélioration des propriétés de transport de l’oxygène et la résistance aux températures de fonctionnement élevées rendent les matériaux d’oxyde à base de lanthane appropriés pour une utilisation dans les cathodes de ce type de piles à combustible. Financé par le programme Actions Marie Skłodowska-Curie, le projet SmartOptoelectronics testera des méthodes d’apprentissage automatique pour étudier la relation entre les propriétés structurelles et les performances électrochimiques des oxydes pérovskites à base de lanthane, sur la base de données expérimentales à haut débit. En explorant l’espace chimique des oxydes à base de lanthane, le projet permettra de concevoir des matériaux aux performances améliorées pour les utiliser dans les μSOFC.
Objectif
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.
Champ scientifique
- 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
Mots‑clés
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-GF - HORIZON TMA MSCA Postdoctoral Fellowships - Global FellowshipsCoordinateur
08930 Sant Adria De Besos
Espagne