Project description
Unravelling the molecular mechanisms that control material self-assembly
Nature uses self-assembly to build fascinating supramolecular materials, such as microtubules and protein filaments, which can self-heal, reconfigure, adapt or respond to specific stimuli dynamically. Building synthetic (polymeric) supramolecular materials that possess similar bio-inspired properties using the same self-assembly principles holds promise for many applications. However, their rational design requires a detailed understanding of the molecular mechanisms that control the self-assembly process, which is typically very difficult to achieve experimentally. The EU-funded DYNAPOL project will shed more light on the molecular origin of the bio-inspired behaviour of these materials using massive multiscale modelling, advanced simulations and machine learning. Research results will lead to fundamental models for the rational design of artificial dynamic materials with controllable bio-inspired properties.
Objective
Nature uses self-assembly to build fascinating supramolecular materials, such as microtubules and protein filaments, that can self-heal, reconfigure, adapt or respond to specific stimuli in dynamic way. Building synthetic (polymeric) supramolecular materials possessing similar bioinspired properties via the same self-assembly principles is interesting for many applications. But their rational design requires a detailed comprehension of the molecular determinants controlling the assembly (structure, dynamics and properties) that is typically very difficult to reach experimentally.
The aim of this project is to obtain structure-dynamics-property relationships to learn how to control the dynamic bioinspired properties of supramolecular polymers. I propose to unravel the molecular origin of the bioinspired behavior through massive multiscale modeling, advanced simulations and machine learning. First, we will develop ad hoc molecular models to study monomer assembly and the supramolecular structure of various types of self-assembled materials on multiple scales. Second, using advanced simulation approaches we will characterize the supramolecular dynamics of these materials (dynamic exchange of monomers) at high (submolecular) resolution. We will then study bioinspired properties such as the ability of various supramolecular materials to self-heal, adapt or reconfigure dynamically in response to specific stimuli. Our models will be systematically validated by comparison with the experimental evidence from our collaborators. Finally, we will use machine learning approaches to analyze our high-resolution simulations and to identify the key monomer features that control and determine the structure, dynamics and dynamic properties of a supramolecular material (i.e. structure-dynamics-property relationships). This research will produce unprecedented insight and fundamental models for the rational design of artificial dynamic materials with controllable bioinspired properties.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: https://op.europa.eu/en/web/eu-vocabularies/euroscivoc.
- natural sciencesbiological sciencesbiochemistrybiomoleculesproteins
- natural scienceschemical sciencespolymer sciences
You need to log in or register to use this function
We are sorry... an unexpected error occurred during execution.
You need to be authenticated. Your session might have expired.
Thank you for your feedback. You will soon receive an email to confirm the submission. If you have selected to be notified about the reporting status, you will also be contacted when the reporting status will change.
Keywords
Programme(s)
Funding Scheme
ERC-COG - Consolidator GrantHost institution
10129 Torino
Italy