Frankenstein's Tiniest Monsters: Inverse Design of Bio-inspired Function in Self-Assembling Materials

Abstract

Despite tremendous advances in synthetic materials design, the complexity achievable in artificial systems is dwarfed by the complexity of living matter. One cause of this discrepancy is that biological systems fundamentally rely on precise control over not just structure, but also function, in micron-scale components. Examples range from kinetic proofreading in DNA to regulation of clathrin formation and on-command microtubule disassembly. Achieving comparable dynamic and non-equilibrium functional control in synthetic materials remains an outstanding challenge. Because biological systems that control these non-equilibrium functionalities exist, it must be possible to design synthetic materials with similarly rich and complex functions. However, the design space of out-of-equilibrium functionalities is vast and hard to explore. How do we design complex functional materials without the luxury of billions of years of evolution? Here, we leverage automatic differentiation, the tool underlying much of the dramatic success in machine learning and non-convex optimization, to develop methods for computational materials design, and demonstrate quantitative control over non-equilibrium functionality in self-assembled materials. We couple this computationally-driven approach with a parallel effort to extract more information from experimental data, towards the goal of making our designs experimentally realizable. We develop a novel algorithm for particle tracking in systems with highly correlated motion and introduce a method for inferring interaction potentials from stochastic trajectory data.