The Automated Design of Materials Far From Equilibrium
This thesis conceptualizes and implements a new framework for designing materials that are far from equilibrium. Starting with state-of-the-art optimization engines, it describes an automated system that makes use of simulations and 3D printing to find the material that best performs a user-specified goal. Identifying which microscopic features produce a desired macroscopic behavior is a problem at the forefront of materials science. This task is materials design, and within it, new goals and challenges have emerged from tailoring the response of materials far from equilibrium. These materials hold promising properties such as robustness, high strength, and self-healing. Yet without a general theory to predict how these properties emerge, designing and controlling them presents a complex and important problem. As proof of concept, the thesis shows how to design the behavior of granular materials, i.e., collections of athermal, macroscopic identical objects, by identifying the particle shapes that form the stiffest, softest, densest, loosest, most dissipative and strain-stiffening aggregates. More generally, the thesis shows how these results serve as prototypes for problems at the heart of materials design, and advocates the perspective that machines are the key to turning complex material forms into new material functions.
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3D-printed adaptive simulated annealing aggregate artificial evolution Automated Design average behavior blueprint rules bonded spheres center of mass CMA-ES collision compact compression configurations convergence coupling constants covariance matrix defined densest design goal Design of Materials design problems design rules dimers distribution energy landscape equilibrium evolution strategies evolutionary algorithm evolutionary computation evolutionary game theory evolve example exponential family framework Gaussian geometries global granular gas granular materials granular molecules interaction strengths inverse problem iteration jammed linear logŒ mechanics microscopic minimize Miskin and Jaeger mutation objects octahedron optimizer packing density packing fraction packing shapes parameterized particle shape payoff function Physical Review plotted procedure produce radii radius random packings randomly replicator equation response rods roughly samples simulated annealing Soft Matter solution solving specific spring constant stiffest and softest stiffness strain-stiffening stress structure task thesis trimer update values vibrational volume fraction