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2.6M - 1. Institut für Theoretische Physik - Universität Stuttgart

2.6M - 1. Institut für Theoretische Physik - Universität Stuttgart

2.6M - 1. Institut für Theoretische Physik - Universität Stuttgart

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Summary<br />

conventions and detailed transformations of several equations. Utmost importance is<br />

attached to practical implementation. The complete printout of the source code of the<br />

used program would not be helpful. However, detailed flowcharts (Nassi-Shneiderman<br />

diagrams [22]) and listings are shown at different places to provide the reader with a<br />

deeper understanding of the simulation method.<br />

This thesis is a part of the project A15 “Numerical Methods for Many-Electron Atoms in<br />

Neutron Star Magnetic Fields” in the context of the “Sonderforschungsbereich” 382 “Procedures<br />

and Algorithms for the Simulation of Physical Processes on High-Performance<br />

Computers” - a joint project of the Universities of Tübingen and <strong>Stuttgart</strong>.<br />

Quantum Monte Carlo Method: The aim was to obtain as accurately as possible the<br />

ground state energy E0 of the many body Schrödinger equation with the Hamiltonian:<br />

ˆH = − 1<br />

N�<br />

�∇<br />

2<br />

i=1<br />

2 i +<br />

� �� �<br />

kinetic energy<br />

1<br />

N�<br />

N�<br />

1<br />

1<br />

− Z<br />

2 |�ri − �rj | |�ri|<br />

i,j<br />

i=1<br />

j �=i<br />

� �� �<br />

� �� � Coulomb potential<br />

Coulomb interaction<br />

. (1)<br />

A statistical approach is given by Quantum Monte Carlo simulations. The technique<br />

is based on walkers moving in 3N -dimensional space. N stands for the number of<br />

electrons of the atom considered and for the neutral atoms treated here is equal to the<br />

atomic number Z . The chapter starts with basics of the Quantum Monte Carlo method<br />

including<br />

� the Variational Principle [23], providing an upper bound (see equation (2.5))<br />

for the energy expectation value ET of a trial function ΨT,<br />

� the Monte Carlo Integration [18], perfectly suitable for the high dimensional<br />

integrals used here,<br />

� the Importance Sampling, increasing efficiency of the Monte Carlo integration<br />

and<br />

� the Metropolis Algorithm [17], making a statement about the acceptance probability<br />

(see equation (2.22)).<br />

The above points lead directly to the Variational Quantum Monte Carlo method<br />

(VQMC, see figure 2.1). This is one of the two simulation techniques used in this thesis.<br />

It yields an upper bound of the ground state energy<br />

126<br />

ET = lim<br />

N →∞<br />

1<br />

N<br />

N�<br />

EL( � R) (2)<br />

i=1

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