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Decoding the Mechanisms Underlying Metal-Organic Frameworks Self-Assembly

Project description

Rational design of metal-organic frameworks

Metal-organic frameworks (MOFs) combine the best of both worlds, organic and inorganic, to yield hybrid crystalline and highly porous structures exquisitely suited to ‘holding’ small molecules to be delivered or removed. Their virtually limitless tuneability cannot be fully exploited using time-consuming trial-and-error synthesis methods. Enhanced understanding of self-assembly mechanisms will facilitate guided control of the formation of building blocks, nucleation and growth. The EU-funded MAGNIFY project will address this critical knowledge gap. The team will develop a multi-scale computational approach to decode the mechanisms underlying MOF self-assembly and predict relationships between synthesis conditions and structure, enabling fast and resource-efficient MOF rational design.

Objective

Metal-Organic Frameworks (MOFs) are porous materials with many societally relevant potential applications, such as carbon capture, removal of environmental toxins and drug-delivery. Despite the progress in the field, synthesizing a MOF currently requires tens to hundreds costly and time-consuming trial-and-error synthesis experiments because our ability to correlate the synthesis conditions with the desired MOF structure is very limited. To overcome this, we need to decode the mechanisms underlying MOF self-assembly, a highly complex non-equilibrium process covering a wide range of time- and length-scales, from the formation of the building units to nucleation and growth.
In MAGNIFY, my team and I will develop a multi-scale computational methodology that will decode the mechanisms underlying MOF self-assembly and predict synthesis conditions-structure relationships. This ambitious interdisciplinary project combines state-of-the-art multi-scale modelling techniques (enhanced sampling techniques, ab initio, atomistic and coarse-graining modelling), with machine-learning approaches to data analysis (dimensionality reduction and data clustering techniques) trained on new chemical descriptors. We will develop and validate our models in tandem with synthesis experiments. We will test our methodology by applying it to two central problems in MOF rational design: (i) determining how synthesis conditions (temperature, solvent, reactants, metal-to-ligand ratio, additives) drive the resulting MOF material's topology and point defects, as well as to (ii) tackling the very challenging task of predicting the synthesis conditions for producing brand new MOFs. This high-risk high-gain project will produce a breakthrough in the MOF field by enabling fast and resource-efficient MOF rational design and will open new research avenues in investigating the self-assembly of other materials and other complex processes happening through a large span of time- and length-scales.

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HORIZON-ERC - HORIZON ERC Grants

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Call for proposal

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(opens in new window) ERC-2021-STG

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Host institution

SORBONNE UNIVERSITE
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 1 340 375,00
Address
21 RUE DE L'ECOLE DE MEDECINE
75006 PARIS
France

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Region
Ile-de-France Ile-de-France Paris
Activity type
Higher or Secondary Education Establishments
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 1 340 375,00

Beneficiaries (2)

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