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Monte Carlo simulation in statistical mechanics is an essential tool of physics,chemistry, and molecular biology, which enables us to predict the properties ofa system in equilibrium at a given temperature. Examples of applicationsinclude the properties of fluids such as surface tension, the binding energiesof drugs to ligands, or predicting the amount of gas adsorbed in a porousmaterial. A standard Monte Carlo simulation predicts properties at a singletemperature. Predicting properties at many temperatures requires running manysimulations and is slow and involves redundant computation. I will present afamily of methods that enable computing the properties of a system at acontinuum of temperatures in a single simulation. These methods dramaticallyreduce the cost to simulate molecular systems over a range of temperatures,thereby accelerating the discovery of molecules and materials, enabling thesimulation of larger systems, and also allowing for more accurate force fields to be used.
Biography: David Roundy is Associate Professor of Physics at Oregon State University. He also studies the properties of condensed matter systems with a focus on fluid systems, via classical density functional theory. He has developed "flat histogram" Monte Carlo methods which allow for more efficient simulation of equilibrium thermal properties. His work in Physics Education Research encompasses two primary emphases - thermal physics and computational physics. He has a B.A. in Physics and Chemistry (1995) and a Ph.D. in Physics (2001), both from the University of California at Berkeley. He held post-doctoral appointments at the Massachusetts Instifute of Technology and Cornell University before joining OSU in 2006.