Data Science for Assembly Engineering

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Abstract

Discovery and design of new materials able to self assemble from nanoscale building blocks are becoming increasingly enabled by large-scale molecular simulation. Aided by fast simulation codes leveraging powerful computer architectures, an unprecedented amount of data can be generated in the blink of an eye, shifting the effort and focus of the computational scientist from the simulation to the data. How do we manage so much data, and what do we do with it when we have it? In this talk, we discuss the applications of data science and data-driven thinking to molecular and materials simulation. Although we do so in the context of assembly engineering of soft matter, the tools and techniques discussed are general and applicable to a wide range of problems. We present applications of machine learning to automated, structure identification of complex colloidal crystals, high-throughput mapping of phase diagrams, the study of kinetic pathways between fluid and solid phases, and the discovery of previously elusive design rules and structure-property relationships. Biography: Sharon C. Glotzer is the John W. Cahn Distinguished University Professor at the University of Michigan, Ann Arbor, the Stuart W. Churchill Collegiate Professor of Chemical Engineering, and the Anthony C. Lembke Department Chair of Chemical Engineering. She is also Professor of Materials Science and Engineering, Physics, Applied Physics, and Macromolecular Science and Engineering. Her research on computational assembly science and engineering aims toward predictive materials design of colloidal and soft matter: using computation, geometrical concepts, and statistical mechanics, her research group seeks to understand complex behavior emerging from simple rules and forces, and use that knowledge to design new classes of materials. Glotzer's group also develops and disseminates powerful open-source software including the particle simulation toolkit, HOOMD-blue, which allows for fast molecular simulation of materials on graphics processors, the signac framework for data and workflow management, and several analysis and visualization tools. Glotzer received her B.S. in Physics from UCLA and her PhD in Physics from Boston University. She is a member of the National Academy of Sciences, the National Academy of Engineering and the American Academy of Arts and Sciences.