SOURCE-LINKED INTELLIGENCE

Knowledge-driven fine-tuning of perovskite-based electrode materials for reversible Chemicals-to-Power devices

CORDIS · observation · Publication date unknown

iculate 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, we also target 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 during the project. To this end, dedicated efforts will be made in certifying the interoperability and usability of the project’s advances in the form of harmonised documentation and open science sharing. spectroscopy, multi-scale modelling, adaptative ML methods, AI, oxides, fuel cells

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recordType

award

status

SIGNED

region

EU

value

5168000

unit

EUR