Minisymposium

Colloidal suspensions confined to emulsion droplets are model systems for understanding crystallization processes and for developing functional materials. They are also an ideal playground for advanced particle simulations and statistical sampling. This presentation focuses on strategies to model such processes in high-performance computing environments using physics-based simulations. Our main tools are event-driven molecular dynamics and Monte Carlo simulation of hard spheres and hard polyhedra. Structure formation is affected by the interplay of thermodynamic and kinetic effects. I will discuss strategies to sample phase space efficiently as required for free-energy calculations, which are a prerequisite to predict phase behavior. Our simulations explain the mesoscale structures in certain minerals, are realized in experiment of clusters with structural color, and explain the self-assembly of certain nanocrystals.

Intrinsic self-healing (SH) polymers can repeatedly heal themselves from mechanical damage by reorganizing their polymer matrices without adding healing reagents. Therefore, they have drawn significant attention to developing sustainable SH soft materials. Recent studies have shown that ion-containing polymers can better harness SH features due to the additional strength of ionic interactions between chains. In this work, we are mainly interested in supramolecular dynamic chemistry, i.e., non-covalent bonding dynamics, as they can repeatedly heal local damage. Although each non-covalent bond is weaker than its covalent counterparts, together, they can form strong polymer network systems and thus achieve high mechanical strength. Particularly, we are interested in the roles of hydrogen bonding and ionic interaction since they are both dynamic and subject to change upon external stimuli, such as heat or UV light. We will discuss how they affect the structural conformation and thermomechanical properties, as well as their competition with ionic interactions.This work was supported by the U.S. Department of Energy (DE-SC0023473) and the U.S. National Science Foundation (NSF Grant No. 2132055).

We present our work on unveiling microscopic details of colloidal and soft matter systems through a novel integration of small-angle neutron scattering (SANS), molecular simulations, computations, and machine learning (ML). First, we demonstrate how ML was employed to invert the scattering of charged colloidal particles to their relevant structural parameters. Molecular dynamics simulations, a probabilistic Gaussian process framework, and a variational autoencoder were trained, and a trained decoder was iteratively applied to fit the input scattering experiment data, thereby closing the loop by transforming experimental SANS data into structural parameters. Similarly, we applied a methodology involving Monte Carlo simulations of AB-type diblock copolymers with excluded volume effects at the dilute limit, utilizing an ML framework of the Gaussian process to inversely determine the conformation of these copolymers from their coherent scattering. Finally, we introduce our newly developed deep learning inversion framework that employs convolutional neural networks to accurately extract morphological features from a model lamella-forming system based on its SANS spectra.

Panel discussion.