Stochastic Decomposition: A New Monte Carlo Algorithm for High-Efficiency Sampling and Applications to Quantum and Classical Problems

University Of Southern California, Los Angeles CA

Investigators

Abstract

Chi Ho H. Mak of the University of Southern California is supported by the Theoretical and Computational Chemistry Program to continue the development and application of a novel and unconventional Monte Carlo method based on the stochastic decomposition algorithm (SDA). Improvements in sampling efficiency allow studies of the statistical mechanics of a variety of complex systems with highly accelerated computational efficiency. Research is underway to include application of path integral simulations to study quantum effects in low-temperature systems, quantum dynamics in condensed-phase systems to study quantum clusters and liquids, membrane- and surface-related phenomena, and dense fluids such as the Lennard-Jones liquid.. Specific applications include the tertiary structure and folding of RNA, DNA packaging in the nucleosome, vesicle budding, fusion and related membrane phenomena, and superfulid helium and hydrogen nanoclusters. This work is having a broader impact in increasing our understanding of the origin of cooperative behavior in complex physical, chemical and biological systems. Students participating in this research receive advanced training in modern methods of computation and simulation.