Andrea Giuntoli did his PhD at the University of Pisa in the group of Dino Leporini, working on computational soft matter and polymer physics. After graduation in 2018, he spent one year as a postdoc at the National Institute of Standards and Technology (NIST), Maryland (USA) with Jack Douglas, then two years at Northwestern University (Chicago) with Sinan Keten within the ChiMaD project (part of the Material Genome Initiative).

In 2021 he joined the Zernike Institute for Advanced Materials as Assistant Professor within the Micromechanics group. His research focuses on the rational design of nanoscale building blocks for advanced polymer composites through the use of theoretical and computational modeling.  

In 2022 he received a grant from the Faculty of Science and Engineering based on his VENI proposal, and an NWO-XS grant.

Publications:

1) A. Giuntoli et al., “Predictive relation for the α-relaxation time of a coarse-grained polymer melt under steady shear”, Sci. Adv. 6 (17), eaaz0777, 2020

The viscosity of non-newtownian fluids is a well-known phenomenon that remains poorly understood at the molecular scale. In this publication we quantitatively predict how the viscosity is related to the molecular relaxation times in model glass forming fluids and we show that the observed shear-thinning is related to the break-down of dynamically heterogeneous molecular clusters.

2) A. Giuntoli, S. Keten, “Tuning star architecture to control mechanical properties and impact resistance of polymer thin films”, Cell Rep. Phys. Sci. 2 (10), 100596, 2021

In this work we show that upon nanoballistic impact, polymer thin films dissipate energy through a 2-stage mechanism related to elastic and plastic deformations, respectively. We use star-shaped polymers to separately control the two responses, leading to more efficient impact-resistant polymer coatings.

3) A. Giuntoli et al., “Systematic coarse-graining of epoxy resins with machine learning-informed energy renormalization”, npj Comput. Mat. 7 (1), 168, 2021

Coarse-grained molecular dynamics models are faster and more efficient than their atomistic counterparts, but at the price of lower accuracy and poor transferability. In this work we developed a coarse-grained model for epoxy resins which captures temperature and crosslinking effects, and on machine learning optimization to correctly determine the force field parameters in high-dimensional paramter space.