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Modeling approaches toward bioinspired dynamic materials

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.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence/machine learning

Call for proposal

ERC-2018-COG
See other projects for this call

Funding Scheme

ERC-COG - Consolidator Grant

Host institution

POLITECNICO DI TORINO
Address
Corso Duca Degli Abruzzi 24
10129 Torino
Italy
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 1 999 623

Beneficiaries (2)

POLITECNICO DI TORINO
Italy
EU contribution
€ 1 999 623
Address
Corso Duca Degli Abruzzi 24
10129 Torino
Activity type
Higher or Secondary Education Establishments
SCUOLA UNIVERSITARIA PROFESSIONALE DELLA SVIZZERA ITALIANA

Participation ended

Switzerland
EU contribution
€ 0
Address
Stabile Le Gerre
6928 Manno
Activity type
Higher or Secondary Education Establishments