459273 Nonlinear Learning of Colloidal Assembly Mechanisms from Simulation and Experiment

Thursday, November 17, 2016: 3:15 PM
Yosemite B (Hilton San Francisco Union Square)
Andrew W. Long1, Jie Zhang1, Steve Granick2 and Andrew L. Ferguson3, (1)Materials Science and Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, (2)Center for Soft and Living Matter, Ulsan National Institute of Science and Technology, Ulsan, Korea, The Republic of, (3)Departments of Materials Science and Engineering and Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL

The inherently many-body nature of assembly makes it a challenge to identify the underlying collective mechanisms and assembly pathways, and has hindered the rational design of building blocks to produce aggregates with desired structure and function. There exists a predictive gulf between individual building block architecture and chemistry, and the thermodynamics and kinetics of collective assembly behavior. By combining tools from statistical mechanics, molecular simulation, and machine learning, we have bridged this divide to systematically identify emergent mechanisms and assembly pathways from molecular simulations or experimental particle tracking data. Our approach furnishes low-dimensional “assembly landscapes” that naturally integrate thermodynamics (what stable aggregates can assemble?) and mechanisms (how do they assemble?) as a function of building block properties.

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.

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See more of this Session: Computational Studies of Self-Assembly
See more of this Group/Topical: Engineering Sciences and Fundamentals