459273 Nonlinear Learning of Colloidal Assembly Mechanisms from Simulation and Experiment
In a first application to experimental particle tracking of the non-equilibrium self-assembly of Janus colloids in an applied AC electric field, we have extracted the underlying assembly landscape directly from experimental data, and used this roadmap to understand assembly and design experimental conditions – electric field strength, AC frequency, salt concentration – to assemble desired aggregate morphologies – pinwheels, clusters, archipelagos. In a second application to Brownian dynamics simulations of "digital colloids" – reconfigurable clusters of freely rotating halo particles tethered to the surface of a central particle – as a novel soft matter-based substrate for high-density information storage, we have extracted the low-dimensional free energy surface governing digital colloid morphology, thermodynamics, and kinetics. By modulating the diameter ratio between halo particles and central particles, we have quantified the reversible work required to write information into the cluster and kinetic stability of the cluster to spontaneous thermal fluctuations. We demonstrate the use of this framework to guide the rational design of a memory storage element to hold a block of text that trades off the competing design criteria of memory addressability and volatility.