Abstract
Colloidal self-assembly provides a scalable route to creating nanomaterials with new architectures and functions. Molecular models and simulations have played an integral role in our understanding of the solvent-mediated interactions between colloidal particles, the assembly morphologies that emerge from these interactions, and the self-assembly process itself. However, detailed molecular models of the building blocks and the solvent are not ideal for exploring assembly behavior or constructing phase diagrams, as the simulations are computational expensive, and the colloidal design space is often vast. Furthermore, the relationship between assembly morphology and design is often complex, so the “inverse design” of particles targeting a given assembly morphology also requires brute-force exploration of the design space. Here, we will demonstrate how machine learning can speed up both the exploration and the targeted design efforts in colloidal assembly. First, we will discuss the development of an analytical potential based on permutationally invariant polynomials for describing the effective multibody interactions between spherical polymer-grafted nanoparticles in a polymer melt. The potential reduces the computational cost of assembly simulations by several orders of magnitude, allowing us to explore assembly behavior over large length and time scales and thereby obtain phases such as strings and hexagonal sheets that cannot be assessed using two-body potentials, and discover novel phases such as networks, clusters, and gels. Next, we will discuss the implementation of a neural adjoint framework for inverse-design of DNA-origami building blocks that can self-assemble into periodic superstructures based on patches of hydrophobic brushes introduced at specific locations on the origamis. Lastly, we will discuss how machine learning can be used for optimizing assembly protocols for improving yield. Our system consists of a binary system of ligand-grafted nanoparticles trapped at a fluid-fluid interface that self-assemble into stripe patterns and quasicrystals. Using a neuroevolutionary algorithm, we determine the optimal temperature annealing profiles that best limit the formation of defects in these assemblies.
