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MIXCONDUCTORS

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Electronic Mesoscale Modeling of Organic Mixed Conductors

The promise of organic materials as biological sensors, electrodes, and memristive devices creates outstanding opportunities for medical diagnosis and treatment, energy storage, as well as neuromorphic computing. At the core of th... see more

31/10/2026

TU/e

203K€

Project Budget: 203K€

Project leader

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

PARTICIPATION DEPralty Sin fecha límite de participación.

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Financing granted El organismo HORIZON EUROPE notifico la concesión del proyecto The day 2024-04-11

HORIZON-MSCA-2023-PF-01-01: MSCA Postdoctoral Fellowships 2023 Expected Outcome:Project results are expected to contribute to the following outcomes:

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

Private Information

No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

TU/e

203.46K€ | Leader

LIU

N.D. | participant

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Project duration: 30 months Date Start: 2024-04-11
End date: 2026-10-31

Project leader

TECHNISCHE UNIVERSITEIT EINDHOVEN No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Project Budget 203K€

participation deadline Sin fecha límite de participación.

Project description The promise of organic materials as biological sensors, electrodes, and memristive devices creates outstanding opportunities for medical diagnosis and treatment, energy storage, as well as neuromorphic computing. At the core of these applications are organic polymers that efficiently support both ionic and electronic transport and therefore are called organic mixed ionic-electronic conductors. These materials’ enabling feature is their ability to convert ionic currents into electronic signals, and vice versa. However, our understanding 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 understanding of the molecular mechanisms behind the functioning of emerging organic mixed ionic-electronic conductors, thereby informing the rational design of improved materials with ramifications for the development of devices that improve health and well-being and enable a future with clean and affordable energy.

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