The advent of petascale computation and the multicore revolution has enabled atomistic simulations of biological and soft-materials systems at previously unimaginable time and length scales. Such calculations offer exciting opportunities to advance fundamental understanding by explicitly modeling the microscopic details that dictate system-level properties. Attendant to this new wealth of data are new challenges in its interpretation. In particular, it remains a difficult task to resolve from the multitudinous atomic degrees of freedom, the collective dynamical mechanisms governing the stability and evolution of the system. We shall discuss our application and development of a nonlinear machine learning approach – the diffusion map – to systematically recover low-dimensional projections of system dynamics into kinetically meaningful collective variables. In analogy with the Mori-Zwanzig projection operator formalism, this embedding captures the slow subspace of the system dynamics, furnishing precisely the lumped variables that govern its long-time scale behavior and evolution. We shall describe our inference of the folding mechanism of an antimicrobial "lasso" peptide, and the self-assembly mechanisms of anisotropic patchy colloidal particles into icosahedral clusters. Finally, we will discuss how our approach offers a computational platform for the rational design of self-assembling materials that intrinsically accounts for both the thermodynamic and kinetic aspects of assembly.
About the Speaker
Andrew L. Ferguson is Assistant Professor of Materials Science and Engineering, and an Affiliated Assistant Professor of Computational Science and Engineering at the University of Illinois at Urbana-Champaign. He received an M.Eng. in Chemical Engineering from Imperial College London in 2005, and a PhD in Chemical and Biological Engineering from Princeton University in 2010. From 2010 to 2012 he was a Postdoctoral Fellow of the Ragon Institute of MGH, MIT, and Harvard in the Department of Chemical Engineering at MIT. He commenced his appointment at UIUC in August 2012. His research interests lie at the intersection of materials science, molecular simulation, and machine learning, with two principal foci: 1) machine learning enabled understanding and design of self-assembling materials including patchy colloids into small molecule encapsulants, and amphiphilic peptides into antimicrobial nanostructures and organic electronic nanowires, 2) bioinformatics inference of statistical mechanical models of viral fitness for computational immunology and therapeutic design. Other areas of interest include the development of global graph matching algorithms and accelerated phase space sampling based on nonlinear machine learning, and computational modeling of membrane penetrating peptides and nanoparticle uptake into tumors.
Host: Martin Ostoja-Starzewski