Feldmann, Michael Todd (2002) Quantum Monte Carlo: Quest to Get Bigger, Faster, and Cheaper. Dissertation (Ph.D.), California Institute of Technology. http://resolver.caltech.edu/CaltechTHESIS:01252012-135136531
We reexamine some fundamental Quantum Monte Carlo (QMC) algorithms with the goal of making QMC more mainstream and efficient. Two major themes exist: (1) Make QMC faster and cheaper, and (2) Make QMC more robust and easier to use. A fast "on-the-fly" algorithm to extract uncorrelated estimators from serially correlated data on a huge network is presented, DDDA. A very efficient manager-worker algorithm for QMC parallelization is presented, QMC-MW. Reduced expense VMC optimization procedure is presented to better guess initial Jastrow parameter sets for hydrocarbons, GJ. I also examine the formation and decomposition of aminomethanol using a variety of methods including a test of the hydrocarbon GJ set on these oxygen- and nitrogen-containing systems. The QMC program suite QMcBeaver is available from the authors in its entirety while a user's and developer's manual is attached as supplementary material.
|Item Type:||Thesis (Dissertation (Ph.D.))|
|Subject Keywords:||Chemistry and Applied Computation|
|Degree Grantor:||California Institute of Technology|
|Division:||Chemistry and Chemical Engineering|
|Minor Option:||Applied And Computational Mathematics|
|Thesis Availability:||Public (worldwide access)|
|Defense Date:||20 May 2002|
|Default Usage Policy:||No commercial reproduction, distribution, display or performance rights in this work are provided.|
|Deposited By:||Benjamin Perez|
|Deposited On:||25 Jan 2012 22:14|
|Last Modified:||10 Feb 2017 22:58|
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