Dr. Riccardo Alessandri - Machine Learning-Enhanced Multiscale Modeling of Soft Electronic Materials

Soft materials with electronic properties offer unique solutions for stretchable electronics, biomedical
sensors, and all-organic batteries. Due to their multiscale nature, the rational design of soft materials with
tailored properties is obscured by the interplay of electronic and structural degrees of freedoms over a
wide range of spatiotemporal scales. This interplay renders quantum mechanical descriptions at
mesoscopic spatiotemporal scales key to accurately predict electronic functionalities in soft materials.
While several bottom-up coarse-graining strategies exist for structural predictions, little has been done
for the development of analogous modeling strategies for electronic structure predictions.
In this talk, I will present computational approaches that combine physics-based and machine learning
techniques, to incorporate electronic structure information at coarse-grained scales. Such approaches will
enable the bottom-up simulation of inherently multiscale soft materials phenomena ranging from ionic
and electronic conduction to degradation. As an example, I will focus on radical-containing, redox-active
polymers, an emerging class of materials for all-organic energy storage devices. Coarse-grained
modeling allows to probe relevant polymeric material length- and timescales. At the same time,
electronic structure information is retained, allowing for the rapid prediction of electronic properties as a
function of material morphology and processing conditions.
Going forward, such machine learning-enhanced multiscale modeling approaches will enable us to tackle
modern soft materials design challenges such as architecting chemical degradability, stretchability, and
mixed conduction in soft electronic materials.