New quantum Monte Carlo algorithms to efficiently utilize massively parallel computers

correlation;diffusion;DMC;hydrocarbon;Jastrow;Monte Carlo;optimization;parallel;parallel correlation;QMC;quantum;quantum Monte Carlo;RDX;serial correlation;statistical analysis;statistics;supercomputing;variational;VMC

Kent, David Randall ; Gray, Harry B. (advisor)

University:California Institute of Technology

Department:Chemistry and Chemical Engineering

关键词: correlation;    diffusion;    DMC;    hydrocarbon;    Jastrow;    Monte Carlo;    optimization;    parallel;    parallel correlation;    QMC;    quantum;    quantum Monte Carlo;    RDX;    serial correlation;    statistical analysis;    statistics;    supercomputing;    variational;    VMC;   

Others  :  https://thesis.library.caltech.edu/748/1/david_randall_kent_iv-dissertation.pdf

美国|英语

来源: Caltech THESIS

【 摘 要 】

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 functionswhich 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.