| KEYWORD |
Molecular modeling of dynamic self-assembling materials with bioinspired properties
keywords COARSE-GRAINING, MACHINE LEARNING, MOLECULAR DYNAMICS, MOLECULAR MODELING AND SIMULATION, MULTISCALE SIMULATION, PHYSICAL CHEMISTRY, SELF-ASSEMBLY, STATISTICAL PHYSICS, SUPRAMOLECULAR CHEMISTRY
Reference persons GIOVANNI MARIA PAVAN
Research Groups COMPUTATIONAL PHYSICAL CHEMISTRY (CPC) LABORATORY
Thesis type MODELING AND SIMULATION
Description Nature uses self-assembly to build fascinating supramolecular materials, such as microtubules and protein filaments, which can self-heal, reconfigure, adapt or dynamically respond to specific stimuli. Understanding how to design artificial supramolecular materials that possess bioinspired properties via similar self-assembly principles would be a breakthrough for many applications, but it is non-trivial.
This thesis is connected to the ERC Consolidator grant project "DYNAPOL", which aims at learning how to design new types of artificial materials with bio-inspired dynamic properties via multiscale molecular models, advanced computational simulation, and machine learning approaches.
See also https://www.gmpavanlab.com/
Notes During the thesis, the student will have the opportunity to work in close contact with worldwide renowned researchers and scientists, national and international academic and industrial partners.
Deadline 07/09/2024
PROPONI LA TUA CANDIDATURA