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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. doi:10.7907/4D4F-WZ34.


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 Jast row 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:
  • Kuppermann, Aron (chair)
  • Bruck, Jehoshua
  • Goddard, William A., III
  • Pierce, Niles A.
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:12 Nov 2021 23:59

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