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Quantum Monte Carlo: Quest to Get Bigger, Faster, and Cheaper


Feldmann, Michael Todd (2002) Quantum Monte Carlo: Quest to Get Bigger, Faster, and Cheaper. Dissertation (Ph.D.), California Institute of Technology.


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
Major Option:Chemistry
Minor Option:Applied And Computational Mathematics
Thesis Availability:Public (worldwide access)
Research Advisor(s):
  • Goddard, William A., III
Thesis Committee:
  • Unknown, Unknown
Defense Date:20 May 2002
Record Number:CaltechTHESIS:01252012-135136531
Persistent URL:
Default Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:6777
Deposited By: Benjamin Perez
Deposited On:25 Jan 2012 22:14
Last Modified:10 Feb 2017 22:58

Thesis Files

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