Descripción del proyecto
Dilucidar los mecanismos moleculares que controlan el autoensamblaje de materiales
La naturaleza emplea el autoensamblaje para construir materiales supramoleculares asombrosos, como los microtúbulos y los filamentos proteicos, que pueden autorrepararse, reconfigurarse, adaptarse o responder dinámicamente a estímulos específicos. El desarrollo de materiales supramoleculares sintéticos (poliméricos) con propiedades bioinspiradas similares utilizando los mismos principios de autoensamblaje resulta prometedor para múltiples aplicaciones. Con todo, su diseño racional requiere una comprensión exhaustiva de los mecanismos moleculares que controlan el proceso de autoensamblaje, que suele ser muy difícil de lograr experimentalmente. El proyecto financiado con fondos europeos DYNAPOL arrojará más luz sobre el origen molecular del comportamiento bioinspirado de estos materiales mediante la modelización masiva a múltiples escalas, el aprendizaje automático y simulaciones avanzadas. Los resultados de la investigación proporcionarán modelos fundamentales para el diseño racional de materiales dinámicos sintéticos con propiedades bioinspiradas controlables.
Objetivo
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.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
ERC-COG - Consolidator GrantInstitución de acogida
10129 Torino
Italia