Project KNOWSKITE-X: Knowledge-driven fine-tuning of perovskite-based electrode materials for reversible chemicals-to-power devices.

The project targets a knowledge-based methodological entry to the finding of new generation electrode materials based on perovskites for reversible SOFC/SOEC technologies. 

Such technologies are archetypal complex systems: the physico-chemical processes at play involve surface electrochemical reactions, ionic diffusion, charge collection and conduction, which all occur timely within a very limited region. Hence, true in-depth understanding of the key parameters requires characterization at the right place, at the right time frame and under the proper operating conditions. The price to pay for achieving this multiply-relevant characterization is the involvement of non-trivial, advanced characterization techniques. The project’s multi-scale modelling approach contributes to turn experimental datasets into a genuine scientific description and make time-saving predictions. 

In the project, the coupling between theoretical and experimental activities is made real by the choice of partners, who are all active in genuinely articulate theory and practice to understand active systems. To provide unifying concepts and to widen the project’s outcomes, intensive collaboration with knowledge discovery using machine-learning and deep learning methods is planned and AI-enabled tools will be used to compensate the smallness of relevant datasets. Such efforts are intended in view of building strong correlations capable of establishing robust composition-structure-activity-performance relations and hence, lead the way to knowledge-based predictions. 

By doing this, the project also targets the implementation of simplified testing protocols and tools operable by industrial stakeholders, which results can be augmented thanks to the knowledge-based pivotal correlations implemented. To this end, dedicated efforts are made in certifying the interoperability and usability of the project’s advances in the form of harmonized documentation and open science sharing.

Our main tasks

• Kinetic modelling.
• Large scale modelling.
• Machine learning and hybrid modelling.
• Strategies relevant to industrial life and innovation requirements.
• Integration of modelling to deep learning to achieve “augmented characterisation”.
• Interoperability of data and data management plan.
• Harmonised workflows.
• Data platform, knowledge architectures and open repository for data transfer.
• Open science knowledge infrastructure.

Partners

C2C-NEWCAP | ELETTRA SINCROTRONE TRIESTE | FIAXELL SOFC TECHNOLOGIES | IDENER.AI | INTERNATIONAL IBERIAN NANOTECHNOLOGY LABORATORY | KNOW-CENTER | NATIONAL CENTER FOR SCIENTIFIC RESEARCH "DEMOKRITOS" | UNIVERSITÀ DI PADOVA | WARRANT HUB

Start date – finish date

01 / 2023 - 12 / 2026