Summary
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.
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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| Web resources: | https://cordis.europa.eu/project/id/101042514 |
| Start date: | 01-12-2022 |
| End date: | 30-11-2027 |
| Total budget - Public funding: | 1 340 375,00 Euro - 1 340 375,00 Euro |
Cordis data
Original description
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.
Status
SIGNEDCall topic
ERC-2021-STGUpdate Date
09-02-2023
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