SOURCE-LINKED INTELLIGENCE

Accelerated Redox Catalysis through AI-driven Design of Electrodes

CORDIS · observation · Publication date unknown

study chemical processes that were previously too complex to examine in detail. Electrocatalysis is one of them, as it plays a central role in producing clean fuels like hydrogen. Today, advances in machine learning (ML) are transforming catalysis studies by bridging the gap between two fields: high-precision quantum calculations, which describe matter at the atomic level, and large-scale classical simulations, which reveal the statistical properties necessary for understanding reactivity. The ARCADE project will leverage these advances to create the first ML framework designed specifically to understand and predict electron transfer reactions, key chemical processes that determine how efficient a material is at driving electrocatalysis. This tool will enable the prediction of the most promising materials for designing improved electrodes, which are essential components of electrochemical reactions driven by sustainable energy. This knowledge will inform the development of more efficient and sustainable hydrogen energy production, tackling a major priority of the European Union in i

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recordType

award

status

SIGNED

region

EU

value

307958.88

unit

EUR