Abstract: Data-driven modeling and machine learning have opened new paradigms and opportunities in the understanding, design, and simulation of soft and biological materials. In the first part of this talk, I will describe our applications of nonlinear manifold learning and evolutionary algorithms to recover and sculpt assembly landscapes for self-assembling patchy colloids. Colloidal particles with tunable anisotropic interactions may be functionalized to spontaneously assemble photonic crystals with omnidirectional band gaps in the visible régime. We have developed an inverse design protocol termed “landscape engineering” to perform automated inverse design of self-assembling diamond and pyrochlore photonic crystals possessing complete, omnidirectional photonic bandgaps.
In the second part of this talk, I will describe our development of a deep learning approach to estimate slow collective variables from molecular simulation trajectories and the use of these coordinates to train highly efficient latent space molecular simulators. The high cost of all-atom calculations restricts molecular simulations to submillisecond timescales. By learning the slowest dynamical modes governing the long-time dynamics from short molecular dynamics simulations, we have trained autonomous dynamical propagators within low-dimensional latent spaces together with generative models of the all-atom configurations to synthesize thermodynamically and dynamically correct simulation trajectories at ~106 lower cost than conventional molecular dynamics calculations.
Bio: Andrew Ferguson is an associate professor at the Pritzker School of Molecular Engineering at the University of Chicago. He received an M.Eng. in chemical engineering from Imperial College London and a Ph.D. in chemical and biological engineering from Princeton University. His research uses theory, simulation, and machine learning to understand and design self-assembling materials, macromolecular folding, and antiviral therapies.