SOURCE-LINKED INTELLIGENCE

Electronic Mesoscale Modeling of Organic Mixed Conductors

CORDIS · observation · Publication date unknown

of these materials is incomplete and the molecular mechanisms underpinning their properties remain elusive. In MIXCONDUCTORS, I will describe and characterize mixed ionic-electronic conductors using machine learning-enhanced multiscale simulations to unravel molecular mechanisms and identify material design guidelines. I propose to use specific machine learning surrogate models to develop a new multiscale method with dramatically increased computational efficiency, unlocking the possibility of bottom-up simulations able to predict device-scale properties. The proposed multiscale method will be used to characterize in silico the growing library of organic mixed conductors, allowing me to uncover their common and/or unique strengths and discover material design guidelines. Finally, together with experimental collaborators, I will be in the position to unravel the molecular mechanisms underpinning some of mixed conductors’ unique properties, enabling me to formulate application-targeted material design guidelines. In summary, MIXCONDUCTORS will provide detailed and unprecedented unders

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recordType

award

status

TERMINATED

region

EU

value

203464.32

unit

EUR