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M4MID

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Toward Desirable Metal Organic Framework Mixed Matrix Materials through Machine...

Toward Desirable Metal Organic Framework Mixed Matrix Materials through Machine learning guided Interface Design Metal Organic Framework Mixed Matrix Materials (M4s) are prototyped for many applications such as separating membranes, drug delivery systems and fire-safe plastics. Like all composites, the premise of M4s is to achieve greater pr... ver más

31/05/2025

IMDEA MATERIALES

181K€

Presupuesto del proyecto: 181K€

Líder del proyecto

IMDEA MATERIALES No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5 | 9M€

Fecha límite participación Sin fecha límite de participación.

Financiación concedida El organismo HORIZON EUROPE notifico la concesión del proyecto el día 2022-06-30

HORIZON-MSCA-2021-PF-01-01: MSCA Postdoctoral Fellowships 2021 ExpectedOutcome:

I+D

Cerrada hace 3 años

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

1 Participantes

IMDEA MATERIALES

181.15K€ | Lider

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Duración del proyecto: 35 meses Fecha Inicio: 2022-06-30
Fecha Fin: 2025-05-31

Líder del proyecto

IMDEA MATERIALES No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5 | 9M€

Presupuesto del proyecto 181K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Metal Organic Framework Mixed Matrix Materials (M4s) are prototyped for many applications such as separating membranes, drug delivery systems and fire-safe plastics. Like all composites, the premise of M4s is to achieve greater properties than those of their isolated components. Yet, the interface between the components determines whether the synergy between the components is achieved or the overall performance is diminished due to their incompatibility. In principle, the interface can be designed by selection and chemical modification of the components involved. However, in practice such design is a very challenging endeavor. Atomistic simulations can predict the structure of the interface, from which the component compatibility may be inferred. Similarly, mechanical testing of the composites may reveal some qualitative characteristics of the interface. Nevertheless, the throughput of these approaches is insufficient to screen the design space for the optimal M4s for various applications. This project aims at developing the capability to computationally design M4s with good mechanical properties. By employing machine learning techniques, we will be able predict the properties of the interface in a high-throughput manner. Similarly, our multi-scale models will establish quantitative relationships between the interfaces and the mechanical properties of the composites, which can be verified experimentally. To achieve these objectives, the Researcher will be provided training-through-research in high-throughput atomistic simulation workflows, material informatics and machine learning for material discovery as well as homogenization techniques and finite element approaches for multi-scale modeling. Moreover, this project will give the Researcher an opportunity to apply her expertise to explore the area of composite design in both basic science and industry-oriented perspective actively undertaken by IMDEA Material Institute.

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