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Type of Document	Dissertation
Author	Kent, David Randall
Author's Email Address	drkent AT users.sourceforge.net
URN	etd-02252003-134943
Persistent URL	http://resolver.caltech.edu/CaltechETD:etd-02252003-134943
Title	New quantum Monte Carlo algorithms to efficiently utilize massively parallel computers
Degree	PhD
Option	Chemistry
Advisory Committee	Advisor Name Title Geoffrey A. Blake Committee Chair Harry B. Gray Committee Member Jehoshua Bruck. Committee Member Nathan S. Lewis Committee Member William A. Goddard III Committee Member
Keywords	quantum Monte Carlo hydrocarbon RDX supercomputing statistical analysis parallel Jastrow optimization statistics correlation diffusion serial correlation parallel correlation QMC Monte Carlo VMC DMC variational quantum
Date of Defense	2003-03-10
Availability	unrestricted
Abstract The exponential growth in computer power over the past few decades has been a huge boon to computational chemistry, physics, biology, and materials science. Now, a standard workstation or Linux cluster can calculate semi-quantitative properties of moderately sized systems. The next step in computational science is developing better algorithms which allow quantitative calculations of a system's properties. A relatively new class of algorithms, known collectively as Quantum Monte Carlo (QMC), has the potential to quantitatively calculate the properties of molecular systems. Furthermore, QMC scales as $O(N^3)$ or better. This makes possible very high-level calculations on systems that are too large to be examined using standard high-level methods. This thesis develops (1) an efficient algorithm for determining "on-the-fly" the statistical error in serially correlated data, (2) a manager-worker parallelization algorithm for QMC that allows calculations to run on heterogeneous parallel computers and computational grids, (3) a robust algorithm for optimizing Jastrow functions which have singularities for some parameter values, and (4) a proof-of-concept demonstrating that it is possible to find transferable parameter sets for large classes of compounds.
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