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2800 lines
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2800 lines
80 KiB
Org Mode
#+TITLE: Quantum Monte Carlo
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#+AUTHOR: Anthony Scemama, Claudia Filippi
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#+LANGUAGE: en
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#+INFOJS_OPT: toc:t mouse:underline path:org-info.js
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#+STARTUP: latexpreview
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#+LATEX_CLASS: report
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#+LATEX_HEADER_EXTRA: \usepackage{minted}
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#+HTML_HEAD: <link rel="stylesheet" title="Standard" href="worg.css" type="text/css" />
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#+OPTIONS: H:4 num:t toc:t \n:nil @:t ::t |:t ^:t -:t f:t *:t <:t
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#+OPTIONS: TeX:t LaTeX:t skip:nil d:nil todo:t pri:nil tags:not-in-toc
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# EXCLUDE_TAGS: solution solution2 noexport
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# EXCLUDE_TAGS: solution noexport
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#+EXCLUDE_TAGS: noexport
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#+BEGIN_SRC elisp :output none :exports none
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(setq org-latex-listings 'minted
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org-latex-packages-alist '(("" "minted"))
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org-latex-pdf-process
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'("pdflatex -shell-escape -interaction nonstopmode -output-directory %o %f"
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"pdflatex -shell-escape -interaction nonstopmode -output-directory %o %f"
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"pdflatex -shell-escape -interaction nonstopmode -output-directory %o %f"))
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(setq org-latex-minted-options '(("breaklines" "true")
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("breakanywhere" "true")))
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(setq org-latex-minted-options
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'(("frame" "lines")
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("fontsize" "\\scriptsize")
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("linenos" "")))
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(org-beamer-export-to-pdf)
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#+END_SRC
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#+RESULTS:
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: /home/scemama/TREX/qmc-lttc/QMC.pdf
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* Introduction
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This website contains the QMC tutorial of the 2021 LTTC winter school
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[[https://www.irsamc.ups-tlse.fr/lttc/Luchon][Tutorials in Theoretical Chemistry]].
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We propose different exercises to understand quantum Monte Carlo (QMC)
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methods. In the first section, we start with the computation of the energy of a
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hydrogen atom using numerical integration. The goal of this section is
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to familarize yourself with the concept of /local energy/.
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Then, we introduce the variational Monte Carlo (VMC) method which
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computes a statistical estimate of the expectation value of the energy
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associated with a given wave function, and apply this approach to the
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hydrogen atom.
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Finally, we present the diffusion Monte Carlo (DMC) method which
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we use here to estimate the exact energy of the hydrogen atom and of the H_2 molecule,
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starting from an approximate wave function.
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Code examples will be given in Python3 and Fortran. You can use
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whatever language you prefer to write the programs.
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We consider the stationary solution of the Schrödinger equation, so
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the wave functions considered here are real: for an $N$ electron
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system where the electrons move in the 3-dimensional space,
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$\Psi : \mathbb{R}^{3N} \rightarrow \mathbb{R}$. In addition, $\Psi$
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is defined everywhere, continuous, and infinitely differentiable.
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All the quantities are expressed in /atomic units/ (energies,
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coordinates, etc).
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** Energy and local energy
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For a given system with Hamiltonian $\hat{H}$ and wave function $\Psi$, we define the local energy as
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$$
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E_L(\mathbf{r}) = \frac{\hat{H} \Psi(\mathbf{r})}{\Psi(\mathbf{r})},
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$$
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where $\mathbf{r}$ denotes the 3N-dimensional electronic coordinates.
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The electronic energy of a system, $E$, can be rewritten in terms of the
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local energy $E_L(\mathbf{r})$ as
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\begin{eqnarray*}
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E & = & \frac{\langle \Psi| \hat{H} | \Psi\rangle}{\langle \Psi |\Psi \rangle}
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= \frac{\int \Psi(\mathbf{r})\, \hat{H} \Psi(\mathbf{r})\, d\mathbf{r}}{\int |\Psi(\mathbf{r}) |^2 d\mathbf{r}} \\
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& = & \frac{\int |\Psi(\mathbf{r})|^2\, \frac{\hat{H} \Psi(\mathbf{r})}{\Psi(\mathbf{r})}\,d\mathbf{r}}{\int |\Psi(\mathbf{r}) |^2 d\mathbf{r}}
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= \frac{\int |\Psi(\mathbf{r})|^2\, E_L(\mathbf{r})\,d\mathbf{r}}{\int |\Psi(\mathbf{r}) |^2 d\mathbf{r}}
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\end{eqnarray*}
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For few dimensions, one can easily compute $E$ by evaluating the
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integrals on a grid but, for a high number of dimensions, one can
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resort to Monte Carlo techniques to compute $E$.
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To this aim, recall that the probabilistic /expected value/ of an
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arbitrary function $f(x)$ with respect to a probability density
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function $P(x)$ is given by
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$$ \langle f \rangle_P = \int_{-\infty}^\infty P(x)\, f(x)\,dx, $$
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where a probability density function $P(x)$ is non-negative
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and integrates to one:
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$$ \int_{-\infty}^\infty P(x)\,dx = 1. $$
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Similarly, we can view the the energy of a system, $E$, as the expected value of the local energy with respect to
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a probability density $P(\mathbf{r})$ defined in 3$N$ dimensions:
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$$ E = \int E_L(\mathbf{r}) P(\mathbf{r})\,d\mathbf{r} \equiv \langle E_L \rangle_{P}\,, $$
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where the probability density is given by the square of the wave function:
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$$ P(\mathbf{r}) = \frac{|\Psi(\mathbf{r})|^2}{\int |\Psi(\mathbf{r})|^2 d\mathbf{r}}\,. $$
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If we can sample $N_{\rm MC}$ configurations $\{\mathbf{r}\}$
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distributed as $P$, we can estimate $E$ as the average of the local
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energy computed over these configurations:
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$$ E \approx \frac{1}{N_{\rm MC}} \sum_{i=1}^{N_{\rm MC}} E_L(\mathbf{r}_i) \,. $$
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* Numerical evaluation of the energy of the hydrogen atom
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In this section, we consider the hydrogen atom with the following
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wave function:
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$$
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\Psi(\mathbf{r}) = \exp(-a |\mathbf{r}|)
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$$
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We will first verify that, for a particular value of $a$, $\Psi$ is an
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eigenfunction of the Hamiltonian
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$$
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\hat{H} = \hat{T} + \hat{V} = - \frac{1}{2} \Delta - \frac{1}{|\mathbf{r}|}
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$$
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To do that, we will compute the local energy and check whether it is constant.
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** Local energy
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:PROPERTIES:
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:header-args:python: :tangle hydrogen.py
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:header-args:f90: :tangle hydrogen.f90
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:END:
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You will now program all quantities needed to compute the local energy of the H atom for the given wave function.
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Write all the functions of this section in a single file : ~hydrogen.py~ if you use Python, or ~hydrogen.f90~ is you use
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Fortran.
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#+begin_note
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- When computing a square root in $\mathbb{R}$, *always* make sure
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that the argument of the square root is non-negative.
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- When you divide, *always* make sure that you will not divide by zero
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If a /floating-point exception/ can occur, you should make a test
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to catch the error.
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#+end_note
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*** Exercise 1
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#+begin_exercise
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Write a function which computes the potential at $\mathbf{r}$.
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The function accepts a 3-dimensional vector =r= as input argument
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and returns the potential.
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#+end_exercise
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$\mathbf{r}=\left( \begin{array}{c} x \\ y\\ z\end{array} \right)$, so
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$$
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V(\mathbf{r}) = -\frac{1}{\sqrt{x^2 + y^2 + z^2}}
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$$ *Python*
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#+BEGIN_SRC python :results none :tangle none
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#!/usr/bin/env python3
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import numpy as np
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def potential(r):
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# TODO
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90 :tangle none
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double precision function potential(r)
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implicit none
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double precision, intent(in) :: r(3)
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! TODO
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end function potential
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#+END_SRC
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**** Solution :solution: *Python*
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#+BEGIN_SRC python :results none
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#!/usr/bin/env python3
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import numpy as np
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def potential(r):
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distance = np.sqrt(np.dot(r,r))
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assert (distance > 0)
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return -1. / distance
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90
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double precision function potential(r)
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implicit none
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double precision, intent(in) :: r(3)
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double precision :: distance
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distance = dsqrt( r(1)*r(1) + r(2)*r(2) + r(3)*r(3) )
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if (distance > 0.d0) then
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potential = -1.d0 / distance
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else
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stop 'potential at r=0.d0 diverges'
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end if
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end function potential
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#+END_SRC
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*** Exercise 2
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#+begin_exercise
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Write a function which computes the wave function at $\mathbf{r}$.
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The function accepts a scalar =a= and a 3-dimensional vector =r= as
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input arguments, and returns a scalar.
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#+end_exercise *Python*
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#+BEGIN_SRC python :results none :tangle none
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def psi(a, r):
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# TODO
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90 :tangle none
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double precision function psi(a, r)
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implicit none
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double precision, intent(in) :: a, r(3)
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! TODO
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end function psi
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#+END_SRC
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**** Solution :solution: *Python*
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#+BEGIN_SRC python :results none
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def psi(a, r):
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return np.exp(-a*np.sqrt(np.dot(r,r)))
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90
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double precision function psi(a, r)
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implicit none
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double precision, intent(in) :: a, r(3)
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psi = dexp(-a * dsqrt( r(1)*r(1) + r(2)*r(2) + r(3)*r(3) ))
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end function psi
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#+END_SRC
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*** Exercise 3
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#+begin_exercise
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Write a function which computes the local kinetic energy at $\mathbf{r}$.
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The function accepts =a= and =r= as input arguments and returns the
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local kinetic energy.
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#+end_exercise
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The local kinetic energy is defined as $$T_L(\mathbf{r}) = -\frac{1}{2}\frac{\Delta \Psi(\mathbf{r})}{\Psi(\mathbf{r})}.$$
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We differentiate $\Psi$ with respect to $x$:
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\[ \Psi(\mathbf{r}) = \exp(-a\,|\mathbf{r}|) \]
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\[\frac{\partial \Psi}{\partial x}
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= \frac{\partial \Psi}{\partial |\mathbf{r}|} \frac{\partial |\mathbf{r}|}{\partial x}
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= - \frac{a\,x}{|\mathbf{r}|} \Psi(\mathbf{r}) \]
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and we differentiate a second time:
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$$
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\frac{\partial^2 \Psi}{\partial x^2} =
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\left( \frac{a^2\,x^2}{|\mathbf{r}|^2} -
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\frac{a(y^2+z^2)}{|\mathbf{r}|^{3}} \right) \Psi(\mathbf{r}).
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$$
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The Laplacian operator $\Delta = \frac{\partial^2}{\partial x^2} +
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\frac{\partial^2}{\partial y^2} + \frac{\partial^2}{\partial z^2}$
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applied to the wave function gives:
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$$
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\Delta \Psi (\mathbf{r}) = \left(a^2 - \frac{2a}{\mathbf{|r|}} \right) \Psi(\mathbf{r})\,.
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$$
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Therefore, the local kinetic energy is
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$$
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T_L (\mathbf{r}) = -\frac{1}{2}\left(a^2 - \frac{2a}{\mathbf{|r|}} \right)
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$$ *Python*
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#+BEGIN_SRC python :results none :tangle none
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def kinetic(a,r):
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# TODO
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90 :tangle none
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double precision function kinetic(a,r)
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implicit none
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double precision, intent(in) :: a, r(3)
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! TODO
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end function kinetic
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#+END_SRC
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**** Solution :solution: *Python*
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#+BEGIN_SRC python :results none
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def kinetic(a,r):
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distance = np.sqrt(np.dot(r,r))
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assert (distance > 0.)
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return a * (1./distance - 0.5 * a)
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90
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double precision function kinetic(a,r)
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implicit none
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double precision, intent(in) :: a, r(3)
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double precision :: distance
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distance = dsqrt( r(1)*r(1) + r(2)*r(2) + r(3)*r(3) )
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if (distance > 0.d0) then
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kinetic = a * (1.d0 / distance - 0.5d0 * a)
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else
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stop 'kinetic energy diverges at r=0'
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end if
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end function kinetic
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#+END_SRC
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*** Exercise 4
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#+begin_exercise
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Write a function which computes the local energy at $\mathbf{r}$,
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using the previously defined functions.
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The function accepts =a= and =r= as input arguments and returns the
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local kinetic energy.
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#+end_exercise
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$$
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E_L(\mathbf{r}) = -\frac{1}{2} \frac{\Delta \Psi}{\Psi} (\mathbf{r}) + V(\mathbf{r})
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$$ *Python*
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#+BEGIN_SRC python :results none :tangle none
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def e_loc(a,r):
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#TODO
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#+END_SRC *Fortran*
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#+begin_note
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When you call a function in Fortran, you need to declare its
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return type.
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You might by accident choose a function name which is the
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same as an internal function of Fortran. So it is recommended to
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*always* use the keyword ~external~ to make sure the function you
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are calling is yours.
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#+end_note
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#+BEGIN_SRC f90 :tangle none
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double precision function e_loc(a,r)
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implicit none
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double precision, intent(in) :: a, r(3)
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double precision, external :: kinetic
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double precision, external :: potential
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! TODO
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end function e_loc
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#+END_SRC
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**** Solution :solution: *Python*
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#+BEGIN_SRC python :results none
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def e_loc(a,r):
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return kinetic(a,r) + potential(r)
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#+END_SRC *Fortran*
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#+BEGIN_SRC f90
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double precision function e_loc(a,r)
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implicit none
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double precision, intent(in) :: a, r(3)
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double precision, external :: kinetic
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double precision, external :: potential
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e_loc = kinetic(a,r) + potential(r)
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end function e_loc
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#+END_SRC
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*** Exercise 5
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#+begin_exercise
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Find the theoretical value of $a$ for which $\Psi$ is an eigenfunction of $\hat{H}$.
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#+end_exercise
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**** Solution :solution:
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\begin{eqnarray*}
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E &=& \frac{\hat{H} \Psi}{\Psi} = - \frac{1}{2} \frac{\Delta \Psi}{\Psi} -
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\frac{1}{|\mathbf{r}|} \\
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&=& -\frac{1}{2}\left(a^2 - \frac{2a}{\mathbf{|r|}} \right) -
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\frac{1}{|\mathbf{r}|} \\
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&=&
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-\frac{1}{2} a^2 + \frac{a-1}{\mathbf{|r|}}
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\end{eqnarray*}
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$a=1$ cancels the $1/|r|$ term, and makes the energy constant and
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equal to -0.5 atomic units.
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** Plot of the local energy along the $x$ axis
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:PROPERTIES:
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:header-args:python: :tangle plot_hydrogen.py
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:header-args:f90: :tangle plot_hydrogen.f90
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:END:
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The program you will write in this section will be written in
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another file (~plot_hydrogen.py~ or ~plot_hydrogen.f90~ for
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example).
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It will use the functions previously defined.
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In Python, you should put at the beginning of the file
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#+BEGIN_SRC python :results none :tangle none
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#!/usr/bin/env python3
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from hydrogen import e_loc
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#+END_SRC
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to be able to use the ~e_loc~ function of the ~hydrogen.py~ file.
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In Fortran, you will need to compile all the source files together:
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#+begin_src sh :exports both
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gfortran hydrogen.f90 plot_hydrogen.f90 -o plot_hydrogen
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#+end_src
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*** Exercise
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#+begin_exercise
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For multiple values of $a$ (0.1, 0.2, 0.5, 1., 1.5, 2.), plot the
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local energy along the $x$ axis. In Python, you can use matplotlib
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for example. In Fortran, it is convenient to write in a text file
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the values of $x$ and $E_L(\mathbf{r})$ for each point, and use
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Gnuplot to plot the files. With Gnuplot, you will need 2 blank
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lines to separate the data corresponding to different values of $a$.
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#+end_exercise
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#+begin_note
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The potential and the kinetic energy both diverge at $r=0$, so we
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choose a grid which does not contain the origin to avoid numerical issues.
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#+end_note *Python*
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#+BEGIN_SRC python :results none :tangle none
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#!/usr/bin/env python3
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import numpy as np
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import matplotlib.pyplot as plt
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from hydrogen import e_loc
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x=np.linspace(-5,5)
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plt.figure(figsize=(10,5))
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# TODO
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plt.tight_layout()
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plt.legend()
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plt.savefig("plot_py.png")
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#+end_src *Fortran*
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#+begin_src f90 :tangle none
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program plot
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implicit none
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double precision, external :: e_loc
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double precision :: x(50), dx
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integer :: i, j
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dx = 10.d0/(size(x)-1)
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do i=1,size(x)
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x(i) = -5.d0 + (i-1)*dx
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end do
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! TODO
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end program plot
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#+end_src
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To compile and run:
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#+begin_src sh :exports both
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gfortran hydrogen.f90 plot_hydrogen.f90 -o plot_hydrogen
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./plot_hydrogen > data
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#+end_src
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To plot the data using Gnuplot:
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#+begin_src gnuplot :file plot.png :exports code
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set grid
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set xrange [-5:5]
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set yrange [-2:1]
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plot './data' index 0 using 1:2 with lines title 'a=0.1', \
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'./data' index 1 using 1:2 with lines title 'a=0.2', \
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'./data' index 2 using 1:2 with lines title 'a=0.5', \
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'./data' index 3 using 1:2 with lines title 'a=1.0', \
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'./data' index 4 using 1:2 with lines title 'a=1.5', \
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'./data' index 5 using 1:2 with lines title 'a=2.0'
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#+end_src
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**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results none
|
||
#!/usr/bin/env python3
|
||
|
||
import numpy as np
|
||
import matplotlib.pyplot as plt
|
||
|
||
from hydrogen import e_loc
|
||
|
||
x=np.linspace(-5,5)
|
||
plt.figure(figsize=(10,5))
|
||
|
||
for a in [0.1, 0.2, 0.5, 1., 1.5, 2.]:
|
||
y=np.array([ e_loc(a, np.array([t,0.,0.]) ) for t in x])
|
||
plt.plot(x,y,label=f"a={a}")
|
||
|
||
plt.tight_layout()
|
||
plt.legend()
|
||
plt.savefig("plot_py.png")
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
|
||
[[./plot_py.png]] *Fortran*
|
||
#+begin_src f90
|
||
program plot
|
||
implicit none
|
||
double precision, external :: e_loc
|
||
|
||
double precision :: x(50), energy, dx, r(3), a(6)
|
||
integer :: i, j
|
||
|
||
a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)
|
||
|
||
dx = 10.d0/(size(x)-1)
|
||
do i=1,size(x)
|
||
x(i) = -5.d0 + (i-1)*dx
|
||
end do
|
||
|
||
r(:) = 0.d0
|
||
|
||
do j=1,size(a)
|
||
print *, '# a=', a(j)
|
||
do i=1,size(x)
|
||
r(1) = x(i)
|
||
energy = e_loc( a(j), r )
|
||
print *, x(i), energy
|
||
end do
|
||
print *, ''
|
||
print *, ''
|
||
end do
|
||
|
||
end program plot
|
||
#+end_src
|
||
|
||
#+begin_src sh :exports none
|
||
gfortran hydrogen.f90 plot_hydrogen.f90 -o plot_hydrogen
|
||
./plot_hydrogen > data
|
||
#+end_src
|
||
|
||
#+begin_src gnuplot :file plot.png :exports results
|
||
set grid
|
||
set xrange [-5:5]
|
||
set yrange [-2:1]
|
||
plot './data' index 0 using 1:2 with lines title 'a=0.1', \
|
||
'./data' index 1 using 1:2 with lines title 'a=0.2', \
|
||
'./data' index 2 using 1:2 with lines title 'a=0.5', \
|
||
'./data' index 3 using 1:2 with lines title 'a=1.0', \
|
||
'./data' index 4 using 1:2 with lines title 'a=1.5', \
|
||
'./data' index 5 using 1:2 with lines title 'a=2.0'
|
||
#+end_src
|
||
#+RESULTS:
|
||
[[file:plot.png]]
|
||
|
||
** Numerical estimation of the energy
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle energy_hydrogen.py
|
||
:header-args:f90: :tangle energy_hydrogen.f90
|
||
:END:
|
||
|
||
If the space is discretized in small volume elements $\mathbf{r}_i$
|
||
of size $\delta \mathbf{r}$, the expression of $\langle E_L \rangle_{\Psi^2}$
|
||
becomes a weighted average of the local energy, where the weights
|
||
are the values of the wave function square at $\mathbf{r}_i$
|
||
multiplied by the volume element:
|
||
|
||
$$
|
||
\langle E \rangle_{\Psi^2} \approx \frac{\sum_i w_i E_L(\mathbf{r}_i)}{\sum_i w_i}, \;\;
|
||
w_i = \left|\Psi(\mathbf{r}_i)\right|^2 \delta \mathbf{r}
|
||
$$
|
||
|
||
#+begin_note
|
||
The energy is biased because:
|
||
- The volume elements are not infinitely small (discretization error)
|
||
- The energy is evaluated only inside the box (incompleteness of the space)
|
||
#+end_note
|
||
|
||
|
||
*** Exercise
|
||
#+begin_exercise
|
||
Compute a numerical estimate of the energy using a grid of
|
||
$50\times50\times50$ points in the range $(-5,-5,-5) \le
|
||
\mathbf{r} \le (5,5,5)$.
|
||
#+end_exercise *Python*
|
||
#+BEGIN_SRC python :results none :tangle none
|
||
#!/usr/bin/env python3
|
||
|
||
import numpy as np
|
||
from hydrogen import e_loc, psi
|
||
|
||
interval = np.linspace(-5,5,num=50)
|
||
delta = (interval[1]-interval[0])**3
|
||
|
||
r = np.array([0.,0.,0.])
|
||
|
||
for a in [0.1, 0.2, 0.5, 0.9, 1., 1.5, 2.]:
|
||
# TODO
|
||
print(f"a = {a} \t E = {E}")
|
||
|
||
#+end_src *Fortran*
|
||
#+begin_src f90
|
||
program energy_hydrogen
|
||
implicit none
|
||
double precision, external :: e_loc, psi
|
||
double precision :: x(50), w, delta, energy, dx, r(3), a(6), norm
|
||
integer :: i, k, l, j
|
||
|
||
a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)
|
||
|
||
dx = 10.d0/(size(x)-1)
|
||
do i=1,size(x)
|
||
x(i) = -5.d0 + (i-1)*dx
|
||
end do
|
||
|
||
do j=1,size(a)
|
||
|
||
! TODO
|
||
|
||
print *, 'a = ', a(j), ' E = ', energy
|
||
end do
|
||
|
||
end program energy_hydrogen
|
||
#+end_src
|
||
|
||
To compile the Fortran and run it:
|
||
|
||
#+begin_src sh :results output :exports code
|
||
gfortran hydrogen.f90 energy_hydrogen.f90 -o energy_hydrogen
|
||
./energy_hydrogen
|
||
#+end_src
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results none :exports both
|
||
#!/usr/bin/env python3
|
||
|
||
import numpy as np
|
||
from hydrogen import e_loc, psi
|
||
|
||
interval = np.linspace(-5,5,num=50)
|
||
delta = (interval[1]-interval[0])**3
|
||
|
||
r = np.array([0.,0.,0.])
|
||
|
||
for a in [0.1, 0.2, 0.5, 0.9, 1., 1.5, 2.]:
|
||
E = 0.
|
||
norm = 0.
|
||
|
||
for x in interval:
|
||
r[0] = x
|
||
for y in interval:
|
||
r[1] = y
|
||
for z in interval:
|
||
r[2] = z
|
||
|
||
w = psi(a,r)
|
||
w = w * w * delta
|
||
|
||
E += w * e_loc(a,r)
|
||
norm += w
|
||
|
||
E = E / norm
|
||
print(f"a = {a} \t E = {E}")
|
||
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: a = 0.1 E = -0.24518438948809218
|
||
: a = 0.2 E = -0.26966057967803525
|
||
: a = 0.5 E = -0.3856357612517407
|
||
: a = 0.9 E = -0.49435709786716214
|
||
: a = 1.0 E = -0.5
|
||
: a = 1.5 E = -0.39242967082602226
|
||
: a = 2.0 E = -0.08086980667844901 *Fortran*
|
||
#+begin_src f90
|
||
program energy_hydrogen
|
||
implicit none
|
||
double precision, external :: e_loc, psi
|
||
double precision :: x(50), w, delta, energy, dx, r(3), a(6), norm
|
||
integer :: i, k, l, j
|
||
|
||
a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)
|
||
|
||
dx = 10.d0/(size(x)-1)
|
||
do i=1,size(x)
|
||
x(i) = -5.d0 + (i-1)*dx
|
||
end do
|
||
|
||
delta = dx**3
|
||
|
||
r(:) = 0.d0
|
||
|
||
do j=1,size(a)
|
||
energy = 0.d0
|
||
norm = 0.d0
|
||
|
||
do i=1,size(x)
|
||
r(1) = x(i)
|
||
|
||
do k=1,size(x)
|
||
r(2) = x(k)
|
||
|
||
do l=1,size(x)
|
||
r(3) = x(l)
|
||
|
||
w = psi(a(j),r)
|
||
w = w * w * delta
|
||
|
||
energy = energy + w * e_loc(a(j), r)
|
||
norm = norm + w
|
||
end do
|
||
|
||
end do
|
||
|
||
end do
|
||
|
||
energy = energy / norm
|
||
print *, 'a = ', a(j), ' E = ', energy
|
||
end do
|
||
|
||
end program energy_hydrogen
|
||
#+end_src
|
||
|
||
#+begin_src sh :results output :exports results
|
||
gfortran hydrogen.f90 energy_hydrogen.f90 -o energy_hydrogen
|
||
./energy_hydrogen
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: a = 0.10000000000000001 E = -0.24518438948809140
|
||
: a = 0.20000000000000001 E = -0.26966057967803236
|
||
: a = 0.50000000000000000 E = -0.38563576125173815
|
||
: a = 1.0000000000000000 E = -0.50000000000000000
|
||
: a = 1.5000000000000000 E = -0.39242967082602065
|
||
: a = 2.0000000000000000 E = -8.0869806678448772E-002
|
||
|
||
** Variance of the local energy
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle variance_hydrogen.py
|
||
:header-args:f90: :tangle variance_hydrogen.f90
|
||
:END:
|
||
|
||
The variance of the local energy is a functional of $\Psi$
|
||
which measures the magnitude of the fluctuations of the local
|
||
energy associated with $\Psi$ around its average:
|
||
|
||
$$
|
||
\sigma^2(E_L) = \frac{\int |\Psi(\mathbf{r})|^2\, \left[
|
||
E_L(\mathbf{r}) - E \right]^2 \, d\mathbf{r}}{\int |\Psi(\mathbf{r}) |^2 d\mathbf{r}}
|
||
$$
|
||
which can be simplified as
|
||
|
||
$$ \sigma^2(E_L) = \langle E_L^2 \rangle_{\Psi^2} - \langle E_L \rangle_{\Psi^2}^2.$$
|
||
|
||
If the local energy is constant (i.e. $\Psi$ is an eigenfunction of
|
||
$\hat{H}$) the variance is zero, so the variance of the local
|
||
energy can be used as a measure of the quality of a wave function.
|
||
|
||
*** Exercise (optional)
|
||
#+begin_exercise
|
||
Prove that :
|
||
$$\langle \left( E - \langle E \rangle_{\Psi^2} \right)^2\rangle_{\Psi^2} = \langle E^2 \rangle_{\Psi^2} - \langle E \rangle_{\Psi^2}^2 $$
|
||
#+end_exercise
|
||
|
||
**** Solution :solution:
|
||
|
||
$\bar{E} = \langle E \rangle$ is a constant, so $\langle \bar{E}
|
||
\rangle = \bar{E}$ .
|
||
|
||
\begin{eqnarray*}
|
||
\langle (E - \bar{E})^2 \rangle & = &
|
||
\langle E^2 - 2 E \bar{E} + \bar{E}^2 \rangle \\
|
||
&=& \langle E^2 \rangle - 2 \langle E \bar{E} \rangle + \langle \bar{E}^2 \rangle \\
|
||
&=& \langle E^2 \rangle - 2 \langle E \rangle \bar{E} + \bar{E}^2 \\
|
||
&=& \langle E^2 \rangle - 2 \bar{E}^2 + \bar{E}^2 \\
|
||
&=& \langle E^2 \rangle - \bar{E}^2 \\
|
||
&=& \langle E^2 \rangle - \langle E \rangle^2 \\
|
||
\end{eqnarray*}
|
||
*** Exercise
|
||
#+begin_exercise
|
||
Add the calculation of the variance to the previous code, and
|
||
compute a numerical estimate of the variance of the local energy using
|
||
a grid of $50\times50\times50$ points in the range $(-5,-5,-5) \le
|
||
\mathbf{r} \le (5,5,5)$ for different values of $a$.
|
||
#+end_exercise *Python*
|
||
#+begin_src python :results none :tangle none
|
||
#!/usr/bin/env python3
|
||
|
||
import numpy as np from hydrogen import e_loc, psi
|
||
|
||
interval = np.linspace(-5,5,num=50)
|
||
|
||
delta = (interval[1]-interval[0])**3
|
||
|
||
r = np.array([0.,0.,0.])
|
||
|
||
for a in [0.1, 0.2, 0.5, 0.9, 1., 1.5, 2.]:
|
||
|
||
# TODO
|
||
|
||
print(f"a = {a} \t E = {E:10.8f} \t \sigma^2 = {s2:10.8f}")
|
||
#+end_src *Fortran*
|
||
#+begin_src f90 :tangle none
|
||
program variance_hydrogen
|
||
implicit none
|
||
|
||
double precision :: x(50), w, delta, energy, energy2
|
||
double precision :: dx, r(3), a(6), norm, e_tmp, s2
|
||
integer :: i, k, l, j
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)
|
||
|
||
dx = 10.d0/(size(x)-1)
|
||
do i=1,size(x)
|
||
x(i) = -5.d0 + (i-1)*dx
|
||
end do
|
||
|
||
do j=1,size(a)
|
||
|
||
! TODO
|
||
|
||
print *, 'a = ', a(j), ' E = ', energy
|
||
end do
|
||
|
||
end program variance_hydrogen
|
||
#+end_src
|
||
|
||
To compile and run:
|
||
|
||
#+begin_src sh :results output :exports both
|
||
gfortran hydrogen.f90 variance_hydrogen.f90 -o variance_hydrogen
|
||
./variance_hydrogen
|
||
#+end_src
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results none :exports both
|
||
#!/usr/bin/env python3
|
||
|
||
import numpy as np
|
||
from hydrogen import e_loc, psi
|
||
|
||
interval = np.linspace(-5,5,num=50)
|
||
|
||
delta = (interval[1]-interval[0])**3
|
||
|
||
r = np.array([0.,0.,0.])
|
||
|
||
for a in [0.1, 0.2, 0.5, 0.9, 1., 1.5, 2.]:
|
||
E = 0.
|
||
E2 = 0.
|
||
norm = 0.
|
||
|
||
for x in interval:
|
||
r[0] = x
|
||
|
||
for y in interval:
|
||
r[1] = y
|
||
|
||
for z in interval:
|
||
r[2] = z
|
||
|
||
w = psi(a,r)
|
||
w = w * w * delta
|
||
|
||
e_tmp = e_loc(a,r)
|
||
E += w * e_tmp
|
||
E2 += w * e_tmp * e_tmp
|
||
norm += w
|
||
|
||
E = E / norm
|
||
E2 = E2 / norm
|
||
|
||
s2 = E2 - E**2
|
||
print(f"a = {a} \t E = {E:10.8f} \t \sigma^2 = {s2:10.8f}")
|
||
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: a = 0.1 E = -0.24518439 \sigma^2 = 0.02696522
|
||
: a = 0.2 E = -0.26966058 \sigma^2 = 0.03719707
|
||
: a = 0.5 E = -0.38563576 \sigma^2 = 0.05318597
|
||
: a = 0.9 E = -0.49435710 \sigma^2 = 0.00577812
|
||
: a = 1.0 E = -0.50000000 \sigma^2 = 0.00000000
|
||
: a = 1.5 E = -0.39242967 \sigma^2 = 0.31449671
|
||
: a = 2.0 E = -0.08086981 \sigma^2 = 1.80688143 *Fortran*
|
||
|
||
#+begin_src f90
|
||
program variance_hydrogen
|
||
implicit none
|
||
|
||
double precision :: x(50), w, delta, energy, energy2
|
||
double precision :: dx, r(3), a(6), norm, e_tmp, s2
|
||
integer :: i, k, l, j
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
a = (/ 0.1d0, 0.2d0, 0.5d0, 1.d0, 1.5d0, 2.d0 /)
|
||
|
||
dx = 10.d0/(size(x)-1)
|
||
do i=1,size(x)
|
||
x(i) = -5.d0 + (i-1)*dx
|
||
end do
|
||
|
||
delta = dx**3
|
||
|
||
r(:) = 0.d0
|
||
|
||
do j=1,size(a)
|
||
energy = 0.d0
|
||
energy2 = 0.d0
|
||
norm = 0.d0
|
||
|
||
do i=1,size(x)
|
||
r(1) = x(i)
|
||
|
||
do k=1,size(x)
|
||
r(2) = x(k)
|
||
|
||
do l=1,size(x)
|
||
r(3) = x(l)
|
||
|
||
w = psi(a(j),r)
|
||
w = w * w * delta
|
||
|
||
e_tmp = e_loc(a(j), r)
|
||
|
||
energy = energy + w * e_tmp
|
||
energy2 = energy2 + w * e_tmp * e_tmp
|
||
norm = norm + w
|
||
end do
|
||
|
||
end do
|
||
|
||
end do
|
||
|
||
energy = energy / norm
|
||
energy2 = energy2 / norm
|
||
|
||
s2 = energy2 - energy*energy
|
||
|
||
print *, 'a = ', a(j), ' E = ', energy, ' s2 = ', s2
|
||
end do
|
||
|
||
end program variance_hydrogen
|
||
#+end_src
|
||
|
||
#+begin_src sh :results output :exports results
|
||
gfortran hydrogen.f90 variance_hydrogen.f90 -o variance_hydrogen
|
||
./variance_hydrogen
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: a = 0.10000000000000001 E = -0.24518438948809140 s2 = 2.6965218719722767E-002
|
||
: a = 0.20000000000000001 E = -0.26966057967803236 s2 = 3.7197072370201284E-002
|
||
: a = 0.50000000000000000 E = -0.38563576125173815 s2 = 5.3185967578480653E-002
|
||
: a = 1.0000000000000000 E = -0.50000000000000000 s2 = 0.0000000000000000
|
||
: a = 1.5000000000000000 E = -0.39242967082602065 s2 = 0.31449670909172917
|
||
: a = 2.0000000000000000 E = -8.0869806678448772E-002 s2 = 1.8068814270846534
|
||
|
||
* Variational Monte Carlo
|
||
|
||
Numerical integration with deterministic methods is very efficient
|
||
in low dimensions. When the number of dimensions becomes large,
|
||
instead of computing the average energy as a numerical integration
|
||
on a grid, it is usually more efficient to use Monte Carlo sampling.
|
||
|
||
Moreover, Monte Carlo sampling will allow us to remove the bias due
|
||
to the discretization of space, and compute a statistical confidence
|
||
interval.
|
||
|
||
** Computation of the statistical error
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle qmc_stats.py
|
||
:header-args:f90: :tangle qmc_stats.f90
|
||
:END:
|
||
|
||
To compute the statistical error, you need to perform $M$
|
||
independent Monte Carlo calculations. You will obtain $M$ different
|
||
estimates of the energy, which are expected to have a Gaussian
|
||
distribution for large $M$, according to the [[https://en.wikipedia.org/wiki/Central_limit_theorem][Central Limit Theorem]].
|
||
|
||
The estimate of the energy is
|
||
|
||
$$
|
||
E = \frac{1}{M} \sum_{i=1}^M E_i
|
||
$$
|
||
|
||
The variance of the average energies can be computed as
|
||
|
||
$$
|
||
\sigma^2 = \frac{1}{M-1} \sum_{i=1}^{M} (E_i - E)^2
|
||
$$
|
||
|
||
And the confidence interval is given by
|
||
|
||
$$
|
||
E \pm \delta E, \text{ where } \delta E = \frac{\sigma}{\sqrt{M}}
|
||
$$
|
||
|
||
*** Exercise
|
||
#+begin_exercise
|
||
Write a function returning the average and statistical error of an
|
||
input array.
|
||
#+end_exercise *Python*
|
||
#+BEGIN_SRC python :results none :tangle none
|
||
#!/usr/bin/env python3
|
||
|
||
from math import sqrt
|
||
def ave_error(arr):
|
||
#TODO
|
||
return (average, error)
|
||
#+END_SRC *Fortran*
|
||
#+BEGIN_SRC f90 :tangle none
|
||
subroutine ave_error(x,n,ave,err)
|
||
implicit none
|
||
integer, intent(in) :: n
|
||
double precision, intent(in) :: x(n)
|
||
double precision, intent(out) :: ave, err
|
||
|
||
! TODO
|
||
|
||
end subroutine ave_error
|
||
#+END_SRC
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results none :exports code
|
||
#!/usr/bin/env python3
|
||
|
||
from math import sqrt
|
||
def ave_error(arr):
|
||
M = len(arr)
|
||
assert(M>0)
|
||
|
||
if M == 1:
|
||
average = arr[0]
|
||
error = 0.
|
||
|
||
else:
|
||
average = sum(arr)/M
|
||
variance = 1./(M-1) * sum( [ (x - average)**2 for x in arr ] )
|
||
error = sqrt(variance/M)
|
||
|
||
return (average, error)
|
||
#+END_SRC *Fortran*
|
||
#+BEGIN_SRC f90 :exports both
|
||
subroutine ave_error(x,n,ave,err)
|
||
implicit none
|
||
|
||
integer, intent(in) :: n
|
||
double precision, intent(in) :: x(n)
|
||
double precision, intent(out) :: ave, err
|
||
|
||
double precision :: variance
|
||
|
||
if (n < 1) then
|
||
stop 'n<1 in ave_error'
|
||
|
||
else if (n == 1) then
|
||
ave = x(1)
|
||
err = 0.d0
|
||
|
||
else
|
||
ave = sum(x(:)) / dble(n)
|
||
|
||
variance = sum((x(:) - ave)**2) / dble(n-1)
|
||
err = dsqrt(variance/dble(n))
|
||
|
||
endif
|
||
end subroutine ave_error
|
||
#+END_SRC
|
||
|
||
** Uniform sampling in the box
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle qmc_uniform.py
|
||
:header-args:f90: :tangle qmc_uniform.f90
|
||
:END:
|
||
|
||
We will now perform our first Monte Carlo calculation to compute the
|
||
energy of the hydrogen atom.
|
||
|
||
Consider again the expression of the energy
|
||
|
||
\begin{eqnarray*}
|
||
E & = & \frac{\int E_L(\mathbf{r})|\Psi(\mathbf{r})|^2\,d\mathbf{r}}{\int |\Psi(\mathbf{r}) |^2 d\mathbf{r}}\,.
|
||
\end{eqnarray*}
|
||
|
||
Clearly, the square of the wave function is a good choice of probability density to sample but we will start with something simpler and rewrite the energy as
|
||
|
||
\begin{eqnarray*}
|
||
E & = & \frac{\int E_L(\mathbf{r})\frac{|\Psi(\mathbf{r})|^2}{P(\mathbf{r})}P(\mathbf{r})\, \,d\mathbf{r}}{\int \frac{|\Psi(\mathbf{r})|^2 }{P(\mathbf{r})}P(\mathbf{r})d\mathbf{r}}\,.
|
||
\end{eqnarray*}
|
||
|
||
Here, we will sample a uniform probability $P(\mathbf{r})$ in a cube of volume $L^3$ centered at the origin:
|
||
|
||
$$ P(\mathbf{r}) = \frac{1}{L^3}\,, $$
|
||
|
||
and zero outside the cube.
|
||
|
||
One Monte Carlo run will consist of $N_{\rm MC}$ Monte Carlo iterations. At every Monte Carlo iteration:
|
||
|
||
- Draw a random point $\mathbf{r}_i$ in the box $(-5,-5,-5) \le
|
||
(x,y,z) \le (5,5,5)$
|
||
- Compute $|\Psi(\mathbf{r}_i)|^2$ and accumulate the result in a
|
||
variable =normalization=
|
||
- Compute $|\Psi(\mathbf{r}_i)|^2 \times E_L(\mathbf{r}_i)$, and accumulate the
|
||
result in a variable =energy=
|
||
|
||
Once all the iterations have been computed, the run returns the average energy
|
||
$\bar{E}_k$ over the $N_{\rm MC}$ iterations of the run.
|
||
|
||
To compute the statistical error, perform $M$ independent runs. The
|
||
final estimate of the energy will be the average over the
|
||
$\bar{E}_k$, and the variance of the $\bar{E}_k$ will be used to
|
||
compute the statistical error.
|
||
|
||
*** Exercise
|
||
|
||
#+begin_exercise
|
||
Parameterize the wave function with $a=1.2$. Perform 30
|
||
independent Monte Carlo runs, each with 100 000 Monte Carlo
|
||
steps. Store the final energies of each run and use this array to
|
||
compute the average energy and the associated error bar.
|
||
#+end_exercise *Python*
|
||
#+begin_note
|
||
To draw a uniform random number in Python, you can use
|
||
the [[https://numpy.org/doc/stable/reference/random/generated/numpy.random.uniform.html][~random.uniform~]] function of Numpy.
|
||
#+end_note
|
||
|
||
#+BEGIN_SRC python :tangle none :exports code
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a, nmax):
|
||
# TODO
|
||
|
||
a = 1.2
|
||
nmax = 100000
|
||
|
||
#TODO
|
||
|
||
print(f"E = {E} +/- {deltaE}")
|
||
#+END_SRC *Fortran*
|
||
#+begin_note
|
||
To draw a uniform random number in Fortran, you can use
|
||
the [[https://gcc.gnu.org/onlinedocs/gfortran/RANDOM_005fNUMBER.html][~RANDOM_NUMBER~]] subroutine.
|
||
#+end_note
|
||
|
||
#+begin_note
|
||
When running Monte Carlo calculations, the number of steps is
|
||
usually very large. We expect =nmax= to be possibly larger than 2
|
||
billion, so we use 8-byte integers (=integer*8=) to represent it, as
|
||
well as the index of the current step.
|
||
#+end_note
|
||
|
||
#+BEGIN_SRC f90 :tangle none
|
||
subroutine uniform_montecarlo(a,nmax,energy)
|
||
implicit none
|
||
double precision, intent(in) :: a
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(out) :: energy
|
||
|
||
integer*8 :: istep
|
||
double precision :: norm, r(3), w
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
! TODO
|
||
end subroutine uniform_montecarlo
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns)
|
||
double precision :: ave, err
|
||
|
||
!TODO
|
||
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports code
|
||
gfortran hydrogen.f90 qmc_stats.f90 qmc_uniform.f90 -o qmc_uniform
|
||
./qmc_uniform
|
||
#+end_src
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results output :exports both
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a, nmax):
|
||
energy = 0.
|
||
normalization = 0.
|
||
|
||
for istep in range(nmax):
|
||
r = np.random.uniform(-5., 5., (3))
|
||
|
||
w = psi(a,r)
|
||
w = w*w
|
||
|
||
energy += w * e_loc(a,r)
|
||
normalization += w
|
||
|
||
return energy / normalization
|
||
|
||
a = 1.2
|
||
nmax = 100000
|
||
|
||
X = [MonteCarlo(a,nmax) for i in range(30)]
|
||
E, deltaE = ave_error(X)
|
||
|
||
print(f"E = {E} +/- {deltaE}")
|
||
#+END_SRC
|
||
|
||
#+RESULTS:
|
||
: E = -0.48363807880008725 +/- 0.002330876047368999
|
||
|
||
*Fortran*
|
||
#+BEGIN_SRC f90 :exports code
|
||
subroutine uniform_montecarlo(a,nmax,energy)
|
||
implicit none
|
||
double precision, intent(in) :: a
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(out) :: energy
|
||
|
||
integer*8 :: istep
|
||
double precision :: norm, r(3), w
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
energy = 0.d0
|
||
norm = 0.d0
|
||
|
||
do istep = 1,nmax
|
||
|
||
call random_number(r)
|
||
r(:) = -5.d0 + 10.d0*r(:)
|
||
|
||
w = psi(a,r)
|
||
w = w*w
|
||
|
||
energy = energy + w * e_loc(a,r)
|
||
norm = norm + w
|
||
|
||
end do
|
||
|
||
energy = energy / norm
|
||
|
||
end subroutine uniform_montecarlo
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call uniform_montecarlo(a, nmax, X(irun))
|
||
enddo
|
||
|
||
call ave_error(X, nruns, ave, err)
|
||
|
||
print *, 'E = ', ave, '+/-', err
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports results
|
||
gfortran hydrogen.f90 qmc_stats.f90 qmc_uniform.f90 -o qmc_uniform
|
||
./qmc_uniform
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: E = -0.48084122147238995 +/- 2.4983775878329355E-003
|
||
|
||
** Metropolis sampling with $\Psi^2$
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle qmc_metropolis.py
|
||
:header-args:f90: :tangle qmc_metropolis.f90
|
||
:END:
|
||
|
||
We will now use the square of the wave function to sample random
|
||
points distributed with the probability density
|
||
\[
|
||
P(\mathbf{r}) = \frac{|\Psi(\mathbf{r})|^2}{\int |\Psi(\mathbf{r})|^2 d\mathbf{r}}\,.
|
||
\]
|
||
|
||
The expression of the average energy is now simplified as the average of
|
||
the local energies, since the weights are taken care of by the
|
||
sampling:
|
||
|
||
$$
|
||
E \approx \frac{1}{N_{\rm MC}}\sum_{i=1}^{N_{\rm MC}} E_L(\mathbf{r}_i)\,.
|
||
$$
|
||
|
||
To sample a chosen probability density, an efficient method is the
|
||
[[https://en.wikipedia.org/wiki/Metropolis%E2%80%93Hastings_algorithm][Metropolis-Hastings sampling algorithm]]. Starting from a random
|
||
initial position $\mathbf{r}_0$, we will realize a random walk:
|
||
|
||
$$ \mathbf{r}_0 \rightarrow \mathbf{r}_1 \rightarrow \mathbf{r}_2 \ldots \rightarrow \mathbf{r}_{N_{\rm MC}}\,, $$
|
||
|
||
according to the following algorithm.
|
||
|
||
At every step, we propose a new move according to a transition probability $T(\mathbf{r}_{n}\rightarrow\mathbf{r}_{n+1})$ of our choice.
|
||
|
||
For simplicity, we will move the electron in a 3-dimensional box of side $2\delta L$ centered at the current position
|
||
of the electron:
|
||
|
||
$$
|
||
\mathbf{r}_{n+1} = \mathbf{r}_{n} + \delta L \, \mathbf{u}
|
||
$$
|
||
|
||
where $\delta L$ is a fixed constant, and
|
||
$\mathbf{u}$ is a uniform random number in a 3-dimensional box
|
||
$(-1,-1,-1) \le \mathbf{u} \le (1,1,1)$.
|
||
|
||
After having moved the electron, we add the
|
||
accept/reject step that guarantees that the distribution of the
|
||
$\mathbf{r}_n$ is $\Psi^2$. This amounts to accepting the move with
|
||
probability
|
||
|
||
$$
|
||
A(\mathbf{r}_{n}\rightarrow\mathbf{r}_{n+1}) = \min\left(1,\frac{T(\mathbf{r}_{n+1}\rightarrow\mathbf{r}_{n}) P(\mathbf{r}_{n+1})}{T(\mathbf{r}_{n}\rightarrow\mathbf{r}_{n+1})P(\mathbf{r}_{n})}\right)\,,
|
||
$$
|
||
|
||
which, for our choice of transition probability, becomes
|
||
|
||
$$
|
||
A(\mathbf{r}_{n}\rightarrow\mathbf{r}_{n+1}) = \min\left(1,\frac{P(\mathbf{r}_{n+1})}{P(\mathbf{r}_{n})}\right)= \min\left(1,\frac{|\Psi(\mathbf{r}_{n+1})|^2}{|\Psi(\mathbf{r}_{n})|^2}\right)\,.
|
||
$$
|
||
|
||
#+begin_exercise
|
||
Explain why the transition probability cancels out in the
|
||
expression of $A$.
|
||
#+end_exercise
|
||
Also note that we do not need to compute the norm of the wave function!
|
||
|
||
The algorithm is summarized as follows:
|
||
|
||
1) Evaluate the local energy at $\mathbf{r}_n$ and accumulate it
|
||
2) Compute a new position $\mathbf{r'} = \mathbf{r}_n + \delta L\, \mathbf{u}$
|
||
3) Evaluate $\Psi(\mathbf{r}')$ at the new position
|
||
4) Compute the ratio $A = \frac{\left|\Psi(\mathbf{r'})\right|^2}{\left|\Psi(\mathbf{r}_{n})\right|^2}$
|
||
5) Draw a uniform random number $v \in [0,1]$
|
||
6) if $v \le A$, accept the move : set $\mathbf{r}_{n+1} = \mathbf{r'}$
|
||
7) else, reject the move : set $\mathbf{r}_{n+1} = \mathbf{r}_n$
|
||
|
||
#+begin_note
|
||
A common error is to remove the rejected samples from the
|
||
calculation of the average. *Don't do it!*
|
||
|
||
All samples should be kept, from both accepted /and/ rejected moves.
|
||
#+end_note
|
||
|
||
|
||
*** Optimal step size
|
||
|
||
If the box is infinitely small, the ratio will be very close
|
||
to one and all the steps will be accepted. However, the moves will be
|
||
very correlated and you will visit the configurational space very slowly.
|
||
|
||
On the other hand, if you propose too large moves, the number of
|
||
accepted steps will decrease because the ratios might become
|
||
small. If the number of accepted steps is close to zero, then the
|
||
space is not well sampled either.
|
||
|
||
The size of the move should be adjusted so that it is as large as
|
||
possible, keeping the number of accepted steps not too small. To
|
||
achieve that, we define the acceptance rate as the number of
|
||
accepted steps over the total number of steps. Adjusting the time
|
||
step such that the acceptance rate is close to 0.5 is a good
|
||
compromise for the current problem.
|
||
|
||
#+begin_note
|
||
Below, we use the symbol $\delta t$ to denote $\delta L$ since we will use
|
||
the same variable later on to store a time step.
|
||
#+end_note
|
||
|
||
|
||
*** Exercise
|
||
|
||
#+begin_exercise
|
||
Modify the program of the previous section to compute the energy,
|
||
sampled with $\Psi^2$.
|
||
|
||
Compute also the acceptance rate, so that you can adapt the time
|
||
step in order to have an acceptance rate close to 0.5.
|
||
|
||
Can you observe a reduction in the statistical error?
|
||
#+end_exercise *Python*
|
||
#+BEGIN_SRC python :results output :tangle none
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a,nmax,dt):
|
||
|
||
# TODO
|
||
|
||
return energy/nmax, N_accep/nmax
|
||
|
||
|
||
# Run simulation
|
||
a = 1.2
|
||
nmax = 100000
|
||
dt = #TODO
|
||
|
||
X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
|
||
|
||
# Energy
|
||
X = [ x for (x, _) in X0 ]
|
||
E, deltaE = ave_error(X)
|
||
print(f"E = {E} +/- {deltaE}")
|
||
|
||
# Acceptance rate
|
||
X = [ x for (_, x) in X0 ]
|
||
A, deltaA = ave_error(X)
|
||
print(f"A = {A} +/- {deltaA}")
|
||
#+END_SRC
|
||
|
||
#+RESULTS: *Fortran*
|
||
#+BEGIN_SRC f90 :tangle none
|
||
subroutine metropolis_montecarlo(a,nmax,dt,energy,accep)
|
||
implicit none
|
||
double precision, intent(in) :: a
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(in) :: dt
|
||
double precision, intent(out) :: energy
|
||
double precision, intent(out) :: accep
|
||
|
||
integer*8 :: istep
|
||
integer*8 :: n_accep
|
||
double precision :: r_old(3), r_new(3), psi_old, psi_new
|
||
double precision :: v, ratio
|
||
|
||
double precision, external :: e_loc, psi, gaussian
|
||
|
||
! TODO
|
||
|
||
end subroutine metropolis_montecarlo
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
double precision, parameter :: dt = ! TODO
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns), Y(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call metropolis_montecarlo(a,nmax,dt,X(irun),Y(irun))
|
||
enddo
|
||
|
||
call ave_error(X,nruns,ave,err)
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
call ave_error(Y,nruns,ave,err)
|
||
print *, 'A = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports both
|
||
gfortran hydrogen.f90 qmc_stats.f90 qmc_metropolis.f90 -o qmc_metropolis
|
||
./qmc_metropolis
|
||
#+end_src
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results output :exports both
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a,nmax,dt):
|
||
energy = 0.
|
||
N_accep = 0
|
||
|
||
r_old = np.random.uniform(-dt, dt, (3))
|
||
psi_old = psi(a,r_old)
|
||
|
||
for istep in range(nmax):
|
||
energy += e_loc(a,r_old)
|
||
|
||
r_new = r_old + np.random.uniform(-dt,dt,(3))
|
||
psi_new = psi(a,r_new)
|
||
|
||
ratio = (psi_new / psi_old)**2
|
||
|
||
if np.random.uniform() <= ratio:
|
||
N_accep += 1
|
||
|
||
r_old = r_new
|
||
psi_old = psi_new
|
||
|
||
return energy/nmax, N_accep/nmax
|
||
|
||
# Run simulation
|
||
a = 1.2
|
||
nmax = 100000
|
||
dt = 1.0
|
||
|
||
X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
|
||
|
||
# Energy
|
||
X = [ x for (x, _) in X0 ]
|
||
E, deltaE = ave_error(X)
|
||
print(f"E = {E} +/- {deltaE}")
|
||
|
||
# Acceptance rate
|
||
X = [ x for (_, x) in X0 ]
|
||
A, deltaA = ave_error(X)
|
||
print(f"A = {A} +/- {deltaA}")
|
||
#+END_SRC
|
||
|
||
#+RESULTS:
|
||
: E = -0.4802595860693983 +/- 0.0005124420418289207
|
||
: A = 0.5074913333333334 +/- 0.000350889422714878 *Fortran*
|
||
#+BEGIN_SRC f90 :exports code
|
||
subroutine metropolis_montecarlo(a,nmax,dt,energy,accep)
|
||
implicit none
|
||
double precision, intent(in) :: a
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(in) :: dt
|
||
double precision, intent(out) :: energy
|
||
double precision, intent(out) :: accep
|
||
|
||
double precision :: r_old(3), r_new(3), psi_old, psi_new
|
||
double precision :: v, ratio
|
||
integer*8 :: n_accep
|
||
integer*8 :: istep
|
||
|
||
double precision, external :: e_loc, psi, gaussian
|
||
|
||
energy = 0.d0
|
||
n_accep = 0_8
|
||
|
||
call random_number(r_old)
|
||
r_old(:) = dt * (2.d0*r_old(:) - 1.d0)
|
||
psi_old = psi(a,r_old)
|
||
|
||
do istep = 1,nmax
|
||
energy = energy + e_loc(a,r_old)
|
||
|
||
call random_number(r_new)
|
||
r_new(:) = r_old(:) + dt*(2.d0*r_new(:) - 1.d0)
|
||
|
||
psi_new = psi(a,r_new)
|
||
|
||
ratio = (psi_new / psi_old)**2
|
||
call random_number(v)
|
||
|
||
if (v <= ratio) then
|
||
|
||
n_accep = n_accep + 1_8
|
||
|
||
r_old(:) = r_new(:)
|
||
psi_old = psi_new
|
||
|
||
endif
|
||
|
||
end do
|
||
|
||
energy = energy / dble(nmax)
|
||
accep = dble(n_accep) / dble(nmax)
|
||
|
||
end subroutine metropolis_montecarlo
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
double precision, parameter :: dt = 1.0d0
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns), Y(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call metropolis_montecarlo(a,nmax,dt,X(irun),Y(irun))
|
||
enddo
|
||
|
||
call ave_error(X,nruns,ave,err)
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
call ave_error(Y,nruns,ave,err)
|
||
print *, 'A = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports results
|
||
gfortran hydrogen.f90 qmc_stats.f90 qmc_metropolis.f90 -o qmc_metropolis
|
||
./qmc_metropolis
|
||
#+end_src
|
||
#+RESULTS:
|
||
: E = -0.47948142754168033 +/- 4.8410177863919307E-004
|
||
: A = 0.50762633333333318 +/- 3.4601756760043725E-004
|
||
|
||
** Generalized Metropolis algorithm
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle vmc_metropolis.py
|
||
:header-args:f90: :tangle vmc_metropolis.f90
|
||
:END:
|
||
|
||
One can use more efficient numerical schemes to move the electrons by choosing a smarter expression for the transition probability.
|
||
|
||
The Metropolis acceptance step has to be adapted accordingly to ensure that the detailed balance condition is satisfied. This means that
|
||
the acceptance probability $A$ is chosen so that it is consistent with the
|
||
probability of leaving $\mathbf{r}_n$ and the probability of
|
||
entering $\mathbf{r}_{n+1}$:
|
||
|
||
\[ A(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) = \min \left( 1,
|
||
\frac{T(\mathbf{r}_{n+1} \rightarrow \mathbf{r}_{n}) P(\mathbf{r}_{n+1})}
|
||
{T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) P(\mathbf{r}_{n})}
|
||
\right)
|
||
\]
|
||
where $T(\mathbf{r}_n \rightarrow \mathbf{r}_{n+1})$ is the
|
||
probability of transition from $\mathbf{r}_n$ to
|
||
$\mathbf{r}_{n+1}$.
|
||
|
||
In the previous example, we were using uniform sampling in a box centered
|
||
at the current position. Hence, the transition probability was symmetric
|
||
|
||
\[
|
||
T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) = T(\mathbf{r}_{n+1} \rightarrow \mathbf{r}_{n})
|
||
= \text{constant}\,,
|
||
\]
|
||
|
||
so the expression of $A$ was simplified to the ratios of the squared
|
||
wave functions.
|
||
|
||
Now, if instead of drawing uniform random numbers, we
|
||
choose to draw Gaussian random numbers with zero mean and variance
|
||
$\delta t$, the transition probability becomes:
|
||
|
||
\[
|
||
T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) =
|
||
\frac{1}{(2\pi\,\delta t)^{3/2}} \exp \left[ - \frac{\left(
|
||
\mathbf{r}_{n+1} - \mathbf{r}_{n} \right)^2}{2\delta t} \right]\,.
|
||
\]
|
||
|
||
|
||
Furthermore, to sample the density even better, we can "push" the electrons
|
||
into in the regions of high probability, and "pull" them away from
|
||
the low-probability regions. This will increase the
|
||
acceptance ratios and improve the sampling.
|
||
|
||
To do this, we can use the gradient of the probability density
|
||
|
||
\[
|
||
\frac{\nabla [ \Psi^2 ]}{\Psi^2} = 2 \frac{\nabla \Psi}{\Psi}\,,
|
||
\]
|
||
|
||
and add the so-called drift vector, $\frac{\nabla \Psi}{\Psi}$, so that the numerical scheme becomes a
|
||
drifted diffusion with transition probability:
|
||
|
||
\[
|
||
T(\mathbf{r}_{n} \rightarrow \mathbf{r}_{n+1}) =
|
||
\frac{1}{(2\pi\,\delta t)^{3/2}} \exp \left[ - \frac{\left(
|
||
\mathbf{r}_{n+1} - \mathbf{r}_{n} - \delta t\frac{\nabla
|
||
\Psi(\mathbf{r}_n)}{\Psi(\mathbf{r}_n)} \right)^2}{2\,\delta t} \right]\,.
|
||
\]
|
||
|
||
The corresponding move is proposed as
|
||
|
||
\[
|
||
\mathbf{r}_{n+1} = \mathbf{r}_{n} + \delta t\, \frac{\nabla
|
||
\Psi(\mathbf{r})}{\Psi(\mathbf{r})} + \chi \,,
|
||
\]
|
||
|
||
where $\chi$ is a Gaussian random variable with zero mean and
|
||
variance $\delta t$.
|
||
|
||
|
||
|
||
The algorithm of the previous exercise is only slighlty modified as:
|
||
|
||
1) Evaluate the local energy at $\mathbf{r}_{n}$ and accumulate it
|
||
2) Compute a new position $\mathbf{r'} = \mathbf{r}_n +
|
||
\delta t\, \frac{\nabla \Psi(\mathbf{r})}{\Psi(\mathbf{r})} + \chi$
|
||
3) Evaluate $\Psi(\mathbf{r}')$ and $\frac{\nabla \Psi(\mathbf{r'})}{\Psi(\mathbf{r'})}$ at the new position
|
||
4) Compute the ratio $A = \frac{T(\mathbf{r}' \rightarrow \mathbf{r}_{n}) P(\mathbf{r}')}{T(\mathbf{r}_{n} \rightarrow \mathbf{r}') P(\mathbf{r}_{n})}$
|
||
5) Draw a uniform random number $v \in [0,1]$
|
||
6) if $v \le A$, accept the move : set $\mathbf{r}_{n+1} = \mathbf{r'}$
|
||
7) else, reject the move : set $\mathbf{r}_{n+1} = \mathbf{r}_n$
|
||
|
||
*** Gaussian random number generator
|
||
|
||
To obtain Gaussian-distributed random numbers, you can apply the
|
||
[[https://en.wikipedia.org/wiki/Box%E2%80%93Muller_transform][Box Muller transform]] to uniform random numbers:
|
||
|
||
\begin{eqnarray*}
|
||
z_1 &=& \sqrt{-2 \ln u_1} \cos(2 \pi u_2) \\
|
||
z_2 &=& \sqrt{-2 \ln u_1} \sin(2 \pi u_2)
|
||
\end{eqnarray*}
|
||
|
||
Below is a Fortran implementation returning a Gaussian-distributed
|
||
n-dimensional vector $\mathbf{z}$. This will be useful for the
|
||
following sections.
|
||
|
||
*Fortran*
|
||
#+BEGIN_SRC f90 :tangle qmc_stats.f90
|
||
subroutine random_gauss(z,n)
|
||
implicit none
|
||
integer, intent(in) :: n
|
||
double precision, intent(out) :: z(n)
|
||
double precision :: u(n+1)
|
||
double precision, parameter :: two_pi = 2.d0*dacos(-1.d0)
|
||
integer :: i
|
||
|
||
call random_number(u)
|
||
|
||
if (iand(n,1) == 0) then
|
||
! n is even
|
||
do i=1,n,2
|
||
z(i) = dsqrt(-2.d0*dlog(u(i)))
|
||
z(i+1) = z(i) * dsin( two_pi*u(i+1) )
|
||
z(i) = z(i) * dcos( two_pi*u(i+1) )
|
||
end do
|
||
|
||
else
|
||
! n is odd
|
||
do i=1,n-1,2
|
||
z(i) = dsqrt(-2.d0*dlog(u(i)))
|
||
z(i+1) = z(i) * dsin( two_pi*u(i+1) )
|
||
z(i) = z(i) * dcos( two_pi*u(i+1) )
|
||
end do
|
||
|
||
z(n) = dsqrt(-2.d0*dlog(u(n)))
|
||
z(n) = z(n) * dcos( two_pi*u(n+1) )
|
||
|
||
end if
|
||
|
||
end subroutine random_gauss
|
||
#+END_SRC
|
||
|
||
In Python, you can use the [[https://numpy.org/doc/stable/reference/random/generated/numpy.random.normal.html][~random.normal~]] function of Numpy.
|
||
|
||
|
||
*** Exercise 1
|
||
|
||
#+begin_exercise
|
||
If you use Fortran, copy/paste the ~random_gauss~ function in
|
||
a Fortran file.
|
||
#+end_exercise
|
||
|
||
#+begin_exercise
|
||
Write a function to compute the drift vector $\frac{\nabla \Psi(\mathbf{r})}{\Psi(\mathbf{r})}$.
|
||
#+end_exercise *Python*
|
||
#+BEGIN_SRC python :tangle hydrogen.py :tangle none
|
||
def drift(a,r):
|
||
# TODO
|
||
#+END_SRC *Fortran*
|
||
#+BEGIN_SRC f90 :tangle hydrogen.f90 :tangle none
|
||
subroutine drift(a,r,b)
|
||
implicit none
|
||
double precision, intent(in) :: a, r(3)
|
||
double precision, intent(out) :: b(3)
|
||
|
||
! TODO
|
||
|
||
end subroutine drift
|
||
#+END_SRC
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :tangle hydrogen.py
|
||
def drift(a,r):
|
||
ar_inv = -a/np.sqrt(np.dot(r,r))
|
||
return r * ar_inv
|
||
#+END_SRC *Fortran*
|
||
#+BEGIN_SRC f90 :tangle hydrogen.f90
|
||
subroutine drift(a,r,b)
|
||
implicit none
|
||
double precision, intent(in) :: a, r(3)
|
||
double precision, intent(out) :: b(3)
|
||
|
||
double precision :: ar_inv
|
||
|
||
ar_inv = -a / dsqrt(r(1)*r(1) + r(2)*r(2) + r(3)*r(3))
|
||
b(:) = r(:) * ar_inv
|
||
|
||
end subroutine drift
|
||
#+END_SRC
|
||
|
||
*** Exercise 2
|
||
|
||
#+begin_exercise
|
||
Modify the previous program to introduce the drift-diffusion scheme.
|
||
(This is a necessary step for the next section on diffusion Monte Carlo).
|
||
#+end_exercise *Python*
|
||
#+BEGIN_SRC python :results output :tangle none
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a,nmax,dt):
|
||
# TODO
|
||
|
||
# Run simulation
|
||
a = 1.2
|
||
nmax = 100000
|
||
dt = # TODO
|
||
|
||
X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
|
||
|
||
# Energy
|
||
X = [ x for (x, _) in X0 ]
|
||
E, deltaE = ave_error(X)
|
||
print(f"E = {E} +/- {deltaE}")
|
||
|
||
# Acceptance rate
|
||
X = [ x for (_, x) in X0 ]
|
||
A, deltaA = ave_error(X)
|
||
print(f"A = {A} +/- {deltaA}")
|
||
#+END_SRC *Fortran*
|
||
#+BEGIN_SRC f90 :tangle none
|
||
subroutine variational_montecarlo(a,dt,nmax,energy,accep)
|
||
implicit none
|
||
double precision, intent(in) :: a, dt
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(out) :: energy, accep
|
||
|
||
integer*8 :: istep
|
||
integer*8 :: n_accep
|
||
double precision :: sq_dt, chi(3)
|
||
double precision :: psi_old, psi_new
|
||
double precision :: r_old(3), r_new(3)
|
||
double precision :: d_old(3), d_new(3)
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
! TODO
|
||
|
||
end subroutine variational_montecarlo
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
double precision, parameter :: dt = ! TODO
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns), accep(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call variational_montecarlo(a,dt,nmax,X(irun),accep(irun))
|
||
enddo
|
||
|
||
call ave_error(X,nruns,ave,err)
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
call ave_error(accep,nruns,ave,err)
|
||
print *, 'A = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports code
|
||
gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
|
||
./vmc_metropolis
|
||
#+end_src
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results output :exports both
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a,nmax,dt):
|
||
sq_dt = np.sqrt(dt)
|
||
|
||
energy = 0.
|
||
N_accep = 0
|
||
|
||
r_old = np.random.normal(loc=0., scale=1.0, size=(3))
|
||
d_old = drift(a,r_old)
|
||
d2_old = np.dot(d_old,d_old)
|
||
psi_old = psi(a,r_old)
|
||
|
||
for istep in range(nmax):
|
||
chi = np.random.normal(loc=0., scale=1.0, size=(3))
|
||
|
||
energy += e_loc(a,r_old)
|
||
|
||
r_new = r_old + dt * d_old + sq_dt * chi
|
||
d_new = drift(a,r_new)
|
||
d2_new = np.dot(d_new,d_new)
|
||
psi_new = psi(a,r_new)
|
||
|
||
# Metropolis
|
||
prod = np.dot((d_new + d_old), (r_new - r_old))
|
||
argexpo = 0.5 * (d2_new - d2_old)*dt + prod
|
||
|
||
q = psi_new / psi_old
|
||
q = np.exp(-argexpo) * q*q
|
||
|
||
if np.random.uniform() <= q:
|
||
N_accep += 1
|
||
|
||
r_old = r_new
|
||
d_old = d_new
|
||
d2_old = d2_new
|
||
psi_old = psi_new
|
||
|
||
return energy/nmax, N_accep/nmax
|
||
|
||
|
||
# Run simulation
|
||
a = 1.2
|
||
nmax = 100000
|
||
dt = 1.0
|
||
|
||
X0 = [ MonteCarlo(a,nmax,dt) for i in range(30)]
|
||
|
||
# Energy
|
||
X = [ x for (x, _) in X0 ]
|
||
E, deltaE = ave_error(X)
|
||
print(f"E = {E} +/- {deltaE}")
|
||
|
||
# Acceptance rate
|
||
X = [ x for (_, x) in X0 ]
|
||
A, deltaA = ave_error(X)
|
||
print(f"A = {A} +/- {deltaA}")
|
||
#+END_SRC
|
||
|
||
#+RESULTS:
|
||
: E = -0.48034171558629885 +/- 0.0005286038561061781
|
||
: A = 0.6210380000000001 +/- 0.0005457375900937905 *Fortran*
|
||
#+BEGIN_SRC f90
|
||
subroutine variational_montecarlo(a,dt,nmax,energy,accep)
|
||
implicit none
|
||
double precision, intent(in) :: a, dt
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(out) :: energy, accep
|
||
|
||
integer*8 :: istep
|
||
integer*8 :: n_accep
|
||
double precision :: sq_dt, chi(3), d2_old, prod, u
|
||
double precision :: psi_old, psi_new, d2_new, argexpo, q
|
||
double precision :: r_old(3), r_new(3)
|
||
double precision :: d_old(3), d_new(3)
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
sq_dt = dsqrt(dt)
|
||
|
||
! Initialization
|
||
energy = 0.d0
|
||
n_accep = 0_8
|
||
|
||
call random_gauss(r_old,3)
|
||
|
||
call drift(a,r_old,d_old)
|
||
d2_old = d_old(1)*d_old(1) + &
|
||
d_old(2)*d_old(2) + &
|
||
d_old(3)*d_old(3)
|
||
|
||
psi_old = psi(a,r_old)
|
||
|
||
do istep = 1,nmax
|
||
energy = energy + e_loc(a,r_old)
|
||
|
||
call random_gauss(chi,3)
|
||
r_new(:) = r_old(:) + dt*d_old(:) + chi(:)*sq_dt
|
||
|
||
call drift(a,r_new,d_new)
|
||
d2_new = d_new(1)*d_new(1) + &
|
||
d_new(2)*d_new(2) + &
|
||
d_new(3)*d_new(3)
|
||
|
||
psi_new = psi(a,r_new)
|
||
|
||
! Metropolis
|
||
prod = (d_new(1) + d_old(1))*(r_new(1) - r_old(1)) + &
|
||
(d_new(2) + d_old(2))*(r_new(2) - r_old(2)) + &
|
||
(d_new(3) + d_old(3))*(r_new(3) - r_old(3))
|
||
|
||
argexpo = 0.5d0 * (d2_new - d2_old)*dt + prod
|
||
|
||
q = psi_new / psi_old
|
||
q = dexp(-argexpo) * q*q
|
||
|
||
call random_number(u)
|
||
|
||
if (u <= q) then
|
||
|
||
n_accep = n_accep + 1_8
|
||
|
||
r_old(:) = r_new(:)
|
||
d_old(:) = d_new(:)
|
||
d2_old = d2_new
|
||
psi_old = psi_new
|
||
|
||
end if
|
||
|
||
end do
|
||
|
||
energy = energy / dble(nmax)
|
||
accep = dble(n_accep) / dble(nmax)
|
||
|
||
end subroutine variational_montecarlo
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
double precision, parameter :: dt = 1.0d0
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns), accep(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call variational_montecarlo(a,dt,nmax,X(irun),accep(irun))
|
||
enddo
|
||
|
||
call ave_error(X,nruns,ave,err)
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
call ave_error(accep,nruns,ave,err)
|
||
print *, 'A = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports results
|
||
gfortran hydrogen.f90 qmc_stats.f90 vmc_metropolis.f90 -o vmc_metropolis
|
||
./vmc_metropolis
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: E = -0.47940635575542706 +/- 5.5613594433433764E-004
|
||
: A = 0.62037333333333333 +/- 4.8970160591451110E-004
|
||
|
||
* Diffusion Monte Carlo
|
||
|
||
As we have seen, Variational Monte Carlo is a powerful method to
|
||
compute integrals in large dimensions. It is often used in cases
|
||
where the expression of the wave function is such that the integrals
|
||
can't be evaluated (multi-centered Slater-type orbitals, correlation
|
||
factors, etc).
|
||
|
||
Diffusion Monte Carlo is different. It goes beyond the computation
|
||
of the integrals associated with an input wave function, and aims at
|
||
finding a near-exact numerical solution to the Schrödinger equation.
|
||
|
||
** Schrödinger equation in imaginary time
|
||
|
||
Consider the time-dependent Schrödinger equation:
|
||
|
||
\[
|
||
i\frac{\partial \Psi(\mathbf{r},t)}{\partial t} = (\hat{H} -E_{\rm ref}) \Psi(\mathbf{r},t)\,.
|
||
\]
|
||
|
||
where we introduced a shift in the energy, $E_{\rm ref}$, for reasons which will become apparent below.
|
||
|
||
We can expand a given starting wave function, $\Psi(\mathbf{r},0)$, in the basis of the eigenstates
|
||
of the time-independent Hamiltonian, $\Phi_k$, with energies $E_k$:
|
||
|
||
\[
|
||
\Psi(\mathbf{r},0) = \sum_k a_k\, \Phi_k(\mathbf{r}).
|
||
\]
|
||
|
||
The solution of the Schrödinger equation at time $t$ is
|
||
|
||
\[
|
||
\Psi(\mathbf{r},t) = \sum_k a_k \exp \left( -i\, (E_k-E_{\rm ref})\, t \right) \Phi_k(\mathbf{r}).
|
||
\]
|
||
|
||
Now, if we replace the time variable $t$ by an imaginary time variable
|
||
$\tau=i\,t$, we obtain
|
||
|
||
\[
|
||
-\frac{\partial \psi(\mathbf{r}, \tau)}{\partial \tau} = (\hat{H} -E_{\rm ref}) \psi(\mathbf{r}, \tau)
|
||
\]
|
||
|
||
where $\psi(\mathbf{r},\tau) = \Psi(\mathbf{r},-i\,\tau)$
|
||
and
|
||
|
||
\begin{eqnarray*}
|
||
\psi(\mathbf{r},\tau) &=& \sum_k a_k \exp( -(E_k-E_{\rm ref})\, \tau) \Phi_k(\mathbf{r})\\
|
||
&=& \exp(-(E_0-E_{\rm ref})\, \tau)\sum_k a_k \exp( -(E_k-E_0)\, \tau) \Phi_k(\mathbf{r})\,.
|
||
\end{eqnarray*}
|
||
|
||
For large positive values of $\tau$, $\psi$ is dominated by the
|
||
$k=0$ term, namely, the lowest eigenstate. If we adjust $E_{\rm ref}$ to the running estimate of $E_0$,
|
||
we can expect that simulating the differetial equation in
|
||
imaginary time will converge to the exact ground state of the
|
||
system.
|
||
|
||
** Relation to diffusion
|
||
|
||
The [[https://en.wikipedia.org/wiki/Diffusion_equation][diffusion equation]] of particles is given by
|
||
|
||
\[
|
||
\frac{\partial \psi(\mathbf{r},t)}{\partial t} = D\, \Delta \psi(\mathbf{r},t)
|
||
\]
|
||
|
||
where $D$ is the diffusion coefficient. When the imaginary-time
|
||
Schrödinger equation is written in terms of the kinetic energy and
|
||
potential,
|
||
|
||
\[
|
||
\frac{\partial \psi(\mathbf{r}, \tau)}{\partial \tau} =
|
||
\left(\frac{1}{2}\Delta - [V(\mathbf{r}) -E_{\rm ref}]\right) \psi(\mathbf{r}, \tau)\,,
|
||
\]
|
||
|
||
it can be identified as the combination of:
|
||
- a diffusion equation (Laplacian)
|
||
- an equation whose solution is an exponential (potential)
|
||
|
||
The diffusion equation can be simulated by a Brownian motion:
|
||
|
||
\[ \mathbf{r}_{n+1} = \mathbf{r}_{n} + \sqrt{\delta t}\, \chi \]
|
||
|
||
where $\chi$ is a Gaussian random variable, and the potential term
|
||
can be simulated by creating or destroying particles over time (a
|
||
so-called branching process) or by simply considering it as a
|
||
cumulative multiplicative weight along the diffusion trajectory
|
||
(pure Diffusion Monte Carlo):
|
||
|
||
\[
|
||
\prod_i \exp \left( - (V(\mathbf{r}_i) - E_{\text{ref}}) \delta t \right).
|
||
\]
|
||
|
||
|
||
We note that the ground-state wave function of a Fermionic system is
|
||
antisymmetric and changes sign. Therefore, its interpretation as a probability
|
||
distribution is somewhat problematic. In fact, mathematically, since
|
||
the Bosonic ground state is lower in energy than the Fermionic one, for
|
||
large $\tau$, the system will evolve towards the Bosonic solution.
|
||
|
||
For the systems you will study, this is not an issue:
|
||
|
||
- Hydrogen atom: You only have one electron!
|
||
- Two-electron system ($H_2$ or He): The ground-wave function is
|
||
antisymmetric in the spin variables but symmetric in the space ones.
|
||
|
||
Therefore, in both cases, you are dealing with a "Bosonic" ground state.
|
||
|
||
** Importance sampling
|
||
|
||
In a molecular system, the potential is far from being constant
|
||
and, in fact, diverges at the inter-particle coalescence points. Hence,
|
||
it results in very large fluctuations of the erm weight associated with
|
||
the potental, making the calculations impossible in practice.
|
||
Fortunately, if we multiply the Schrödinger equation by a chosen /trial wave function/ $\Psi_T(\mathbf{r})$ (Hartree-Fock, Kohn-Sham
|
||
determinant, CI wave function, /etc/), one obtains
|
||
|
||
\[
|
||
-\frac{\partial \psi(\mathbf{r},\tau)}{\partial \tau} \Psi_T(\mathbf{r}) =
|
||
\left[ -\frac{1}{2} \Delta \psi(\mathbf{r},\tau) + V(\mathbf{r}) \psi(\mathbf{r},\tau) \right] \Psi_T(\mathbf{r})
|
||
\]
|
||
|
||
Defining $\Pi(\mathbf{r},\tau) = \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})$, (see appendix for details)
|
||
|
||
\[
|
||
-\frac{\partial \Pi(\mathbf{r},\tau)}{\partial \tau}
|
||
= -\frac{1}{2} \Delta \Pi(\mathbf{r},\tau) +
|
||
\nabla \left[ \Pi(\mathbf{r},\tau) \frac{\nabla \Psi_T(\mathbf{r})}{\Psi_T(\mathbf{r})}
|
||
\right] + (E_L(\mathbf{r})-E_{\rm ref})\Pi(\mathbf{r},\tau)
|
||
\]
|
||
|
||
The new "kinetic energy" can be simulated by the drift-diffusion
|
||
scheme presented in the previous section (VMC).
|
||
The new "potential" is the local energy, which has smaller fluctuations
|
||
when $\Psi_T$ gets closer to the exact wave function.
|
||
This term can be simulated by
|
||
\[
|
||
\prod_i \exp \left( - (E_L(\mathbf{r}_i) - E_{\text{ref}}) \delta t \right).
|
||
\]
|
||
where $E_{\rm ref}$ is the constant we had introduced above, which is adjusted to
|
||
an estimate of the average energy to keep the weights close to one.
|
||
|
||
This equation generates the /N/-electron density $\Pi$, which is the
|
||
product of the ground state solution with the trial wave
|
||
function. You may then ask: how can we compute the total energy of
|
||
the system?
|
||
|
||
To this aim, we use the /mixed estimator/ of the energy:
|
||
|
||
\begin{eqnarray*}
|
||
E(\tau) &=& \frac{\langle \psi(\tau) | \hat{H} | \Psi_T \rangle}{\langle \psi(\tau) | \Psi_T \rangle}\\
|
||
&=& \frac{\int \psi(\mathbf{r},\tau) \hat{H} \Psi_T(\mathbf{r}) d\mathbf{r}}
|
||
{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) d\mathbf{r}} \\
|
||
&=& \frac{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) E_L(\mathbf{r}) d\mathbf{r}}
|
||
{\int \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) d\mathbf{r}} \,.
|
||
\end{eqnarray*}
|
||
|
||
For large $\tau$, we have that
|
||
|
||
\[
|
||
\Pi(\mathbf{r},\tau) =\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \rightarrow \Phi_0(\mathbf{r}) \Psi_T(\mathbf{r})\,,
|
||
\]
|
||
|
||
and, using that $\hat{H}$ is Hermitian and that $\Phi_0$ is an
|
||
eigenstate of the Hamiltonian, we obtain for large $\tau$
|
||
|
||
\[
|
||
E(\tau) = \frac{\langle \psi_\tau | \hat{H} | \Psi_T \rangle}
|
||
{\langle \psi_\tau | \Psi_T \rangle}
|
||
= \frac{\langle \Psi_T | \hat{H} | \psi_\tau \rangle}
|
||
{\langle \Psi_T | \psi_\tau \rangle}
|
||
\rightarrow E_0 \frac{\langle \Psi_T | \Phi_0 \rangle}
|
||
{\langle \Psi_T | \Phi_0 \rangle}
|
||
= E_0
|
||
\]
|
||
|
||
Therefore, we can compute the energy within DMC by generating the
|
||
density $\Pi$ with random walks, and simply averaging the local
|
||
energies computed with the trial wave function.
|
||
|
||
*** Appendix : Details of the Derivation
|
||
|
||
\[
|
||
-\frac{\partial \psi(\mathbf{r},\tau)}{\partial \tau} \Psi_T(\mathbf{r}) =
|
||
\left[ -\frac{1}{2} \Delta \psi(\mathbf{r},\tau) + V(\mathbf{r}) \psi(\mathbf{r},\tau) \right] \Psi_T(\mathbf{r})
|
||
\]
|
||
|
||
\[
|
||
-\frac{\partial \big[ \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big]}{\partial \tau}
|
||
= -\frac{1}{2} \Big( \Delta \big[
|
||
\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big] -
|
||
\psi(\mathbf{r},\tau) \Delta \Psi_T(\mathbf{r}) - 2
|
||
\nabla \psi(\mathbf{r},\tau) \nabla \Psi_T(\mathbf{r}) \Big) + V(\mathbf{r}) \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})
|
||
\]
|
||
|
||
\[
|
||
-\frac{\partial \big[ \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big]}{\partial \tau}
|
||
= -\frac{1}{2} \Delta \big[\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big] +
|
||
\frac{1}{2} \psi(\mathbf{r},\tau) \Delta \Psi_T(\mathbf{r}) +
|
||
\Psi_T(\mathbf{r})\nabla \psi(\mathbf{r},\tau) \frac{\nabla \Psi_T(\mathbf{r})}{\Psi_T(\mathbf{r})} + V(\mathbf{r}) \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})
|
||
\]
|
||
|
||
\[
|
||
-\frac{\partial \big[ \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big]}{\partial \tau}
|
||
= -\frac{1}{2} \Delta \big[\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big] +
|
||
\psi(\mathbf{r},\tau) \Delta \Psi_T(\mathbf{r}) +
|
||
\Psi_T(\mathbf{r})\nabla \psi(\mathbf{r},\tau) \frac{\nabla \Psi_T(\mathbf{r})}{\Psi_T(\mathbf{r})} + E_L(\mathbf{r}) \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})
|
||
\]
|
||
\[
|
||
-\frac{\partial \big[ \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big]}{\partial \tau}
|
||
= -\frac{1}{2} \Delta \big[\psi(\mathbf{r},\tau) \Psi_T(\mathbf{r}) \big] +
|
||
\nabla \left[ \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})
|
||
\frac{\nabla \Psi_T(\mathbf{r})}{\Psi_T(\mathbf{r})}
|
||
\right] + E_L(\mathbf{r}) \psi(\mathbf{r},\tau) \Psi_T(\mathbf{r})
|
||
\]
|
||
|
||
Defining $\Pi(\mathbf{r},t) = \psi(\mathbf{r},\tau)
|
||
\Psi_T(\mathbf{r})$,
|
||
|
||
\[
|
||
-\frac{\partial \Pi(\mathbf{r},\tau)}{\partial \tau}
|
||
= -\frac{1}{2} \Delta \Pi(\mathbf{r},\tau) +
|
||
\nabla \left[ \Pi(\mathbf{r},\tau) \frac{\nabla \Psi_T(\mathbf{r})}{\Psi_T(\mathbf{r})}
|
||
\right] + E_L(\mathbf{r}) \Pi(\mathbf{r},\tau)
|
||
\]
|
||
|
||
** Pure Diffusion Monte Carlo
|
||
|
||
Instead of having a variable number of particles to simulate the
|
||
branching process as in the /Diffusion Monte Carlo/ (DMC) algorithm, we
|
||
use variant called /pure Diffusion Monte Carlo/ (PDMC) where
|
||
the potential term is considered as a cumulative product of weights:
|
||
|
||
\begin{eqnarray*}
|
||
W(\mathbf{r}_n, \tau) = \prod_{i=1}^{n} \exp \left( -\delta t\,
|
||
(E_L(\mathbf{r}_i) - E_{\text{ref}}) \right) =
|
||
\prod_{i=1}^{n} w(\mathbf{r}_i)
|
||
\end{eqnarray*}
|
||
|
||
where $\mathbf{r}_i$ are the coordinates along the trajectory and
|
||
we introduced a /time-step variable/ $\delta t$ to discretize the
|
||
integral.
|
||
|
||
The PDMC algorithm is less stable than the DMC algorithm: it
|
||
requires to have a value of $E_\text{ref}$ which is close to the
|
||
fixed-node energy, and a good trial wave function. Moreover, we
|
||
can't let $\tau$ become too large because the weight whether
|
||
explode or vanish: we need to have a fixed value of $\tau$
|
||
(projection time).
|
||
The big advantage of PDMC is that it is rather simple to implement
|
||
starting from a VMC code:
|
||
|
||
0) Start with $W(\mathbf{r}_0)=1, \tau_0 = 0$
|
||
1) Evaluate the local energy at $\mathbf{r}_{n}$
|
||
2) Compute the contribution to the weight $w(\mathbf{r}_n) =
|
||
\exp(-\delta t(E_L(\mathbf{r}_n)-E_\text{ref}))$
|
||
3) Update $W(\mathbf{r}_{n}) = W(\mathbf{r}_{n-1}) \times w(\mathbf{r}_n)$
|
||
4) Accumulate the weighted energy $W(\mathbf{r}_n) \times
|
||
E_L(\mathbf{r}_n)$,
|
||
and the weight $W(\mathbf{r}_n)$ for the normalization
|
||
5) Update $\tau_n = \tau_{n-1} + \delta t$
|
||
6) If $\tau_{n} > \tau_\text{max}$ ($\tau_\text{max}$ is an input parameter), the long projection time has
|
||
been reached and we can start an new trajectory from the current
|
||
position: reset $W(r_n) = 1$ and $\tau_n
|
||
= 0$
|
||
7) Compute a new position $\mathbf{r'} = \mathbf{r}_n +
|
||
\delta t\, \frac{\nabla \Psi(\mathbf{r})}{\Psi(\mathbf{r})} + \chi$
|
||
8) Evaluate $\Psi(\mathbf{r}')$ and $\frac{\nabla \Psi(\mathbf{r'})}{\Psi(\mathbf{r'})}$ at the new position
|
||
9) Compute the ratio $A = \frac{T(\mathbf{r}' \rightarrow \mathbf{r}_{n}) P(\mathbf{r}')}{T(\mathbf{r}_{n} \rightarrow \mathbf{r}') P(\mathbf{r}_{n})}$
|
||
10) Draw a uniform random number $v \in [0,1]$
|
||
11) if $v \le A$, accept the move : set $\mathbf{r}_{n+1} = \mathbf{r'}$
|
||
12) else, reject the move : set $\mathbf{r}_{n+1} = \mathbf{r}_n$
|
||
|
||
|
||
Some comments are needed:
|
||
|
||
- You estimate the energy as
|
||
|
||
\begin{eqnarray*}
|
||
E = \frac{\sum_{k=1}^{N_{\rm MC}} E_L(\mathbf{r}_k) W(\mathbf{r}_k, k\delta t)}{\sum_{k=1}^{N_{\rm MC}} W(\mathbf{r}_k, k\delta t)}
|
||
\end{eqnarray*}
|
||
|
||
- The result will be affected by a time-step error
|
||
(the finite size of $\delta t$) due to the discretization of the
|
||
integral, and one has in principle to extrapolate to the limit
|
||
$\delta t \rightarrow 0$. This amounts to fitting the energy
|
||
computed for multiple values of $\delta t$.
|
||
|
||
Here, you will be using a small enough time-step and you should not worry about the extrapolation.
|
||
- The accept/reject step (steps 9-12 in the algorithm) is in principle not needed for the correctness of
|
||
the DMC algorithm. However, its use reduces significantly the time-step error.
|
||
|
||
|
||
** Hydrogen atom
|
||
:PROPERTIES:
|
||
:header-args:python: :tangle pdmc.py
|
||
:header-args:f90: :tangle pdmc.f90
|
||
:END:
|
||
|
||
*** Exercise
|
||
|
||
#+begin_exercise
|
||
Modify the Metropolis VMC program into a PDMC program.
|
||
In the limit $\delta t \rightarrow 0$, you should recover the exact
|
||
energy of H for any value of $a$, as long as the simulation is stable.
|
||
We choose here a time step of 0.05 a.u. and a fixed projection
|
||
time $\tau_{\text{max}}$ =100 a.u.
|
||
#+end_exercise *Python*
|
||
#+BEGIN_SRC python :results output
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a, nmax, dt, Eref):
|
||
# TODO
|
||
|
||
# Run simulation
|
||
a = 1.2
|
||
nmax = 100000
|
||
dt = 0.05
|
||
tau = 100.
|
||
E_ref = -0.5
|
||
|
||
X0 = [ MonteCarlo(a, nmax, dt, E_ref) for i in range(30)]
|
||
|
||
# Energy
|
||
X = [ x for (x, _) in X0 ]
|
||
E, deltaE = ave_error(X)
|
||
print(f"E = {E} +/- {deltaE}")
|
||
|
||
# Acceptance rate
|
||
X = [ x for (_, x) in X0 ]
|
||
A, deltaA = ave_error(X)
|
||
print(f"A = {A} +/- {deltaA}")
|
||
#+END_SRC
|
||
|
||
#+RESULTS: *Fortran*
|
||
#+BEGIN_SRC f90 :tangle none
|
||
subroutine pdmc(a, dt, nmax, energy, accep, tau, E_ref)
|
||
implicit none
|
||
double precision, intent(in) :: a, dt, tau
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(out) :: energy, accep
|
||
double precision, intent(in) :: E_ref
|
||
|
||
integer*8 :: istep
|
||
integer*8 :: n_accep
|
||
double precision :: sq_dt, chi(3)
|
||
double precision :: psi_old, psi_new
|
||
double precision :: r_old(3), r_new(3)
|
||
double precision :: d_old(3), d_new(3)
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
! TODO
|
||
|
||
end subroutine pdmc
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
double precision, parameter :: dt = 0.05d0
|
||
double precision, parameter :: E_ref = -0.5d0
|
||
double precision, parameter :: tau = 100.d0
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns), accep(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call pdmc(a, dt, nmax, X(irun), accep(irun), tau, E_ref)
|
||
enddo
|
||
|
||
call ave_error(X,nruns,ave,err)
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
call ave_error(accep,nruns,ave,err)
|
||
print *, 'A = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports code
|
||
gfortran hydrogen.f90 qmc_stats.f90 pdmc.f90 -o pdmc
|
||
./pdmc
|
||
#+end_src
|
||
|
||
**** Solution :solution: *Python*
|
||
#+BEGIN_SRC python :results output
|
||
#!/usr/bin/env python3
|
||
|
||
from hydrogen import *
|
||
from qmc_stats import *
|
||
|
||
def MonteCarlo(a, nmax, dt, tau, Eref):
|
||
sq_dt = np.sqrt(dt)
|
||
|
||
energy = 0.
|
||
N_accep = 0
|
||
normalization = 0.
|
||
|
||
w = 1.
|
||
tau_current = 0.
|
||
|
||
r_old = np.random.normal(loc=0., scale=1.0, size=(3))
|
||
d_old = drift(a,r_old)
|
||
d2_old = np.dot(d_old,d_old)
|
||
psi_old = psi(a,r_old)
|
||
|
||
for istep in range(nmax):
|
||
el = e_loc(a,r_old)
|
||
w *= np.exp(-dt*(el - Eref))
|
||
|
||
normalization += w
|
||
energy += w * el
|
||
|
||
tau_current += dt
|
||
|
||
# Reset when tau is reached
|
||
if tau_current > tau:
|
||
w = 1.
|
||
tau_current = 0.
|
||
|
||
chi = np.random.normal(loc=0., scale=1.0, size=(3))
|
||
|
||
r_new = r_old + dt * d_old + sq_dt * chi
|
||
d_new = drift(a,r_new)
|
||
d2_new = np.dot(d_new,d_new)
|
||
psi_new = psi(a,r_new)
|
||
|
||
# Metropolis
|
||
prod = np.dot((d_new + d_old), (r_new - r_old))
|
||
argexpo = 0.5 * (d2_new - d2_old)*dt + prod
|
||
|
||
q = psi_new / psi_old
|
||
q = np.exp(-argexpo) * q*q
|
||
|
||
if np.random.uniform() <= q:
|
||
N_accep += 1
|
||
r_old = r_new
|
||
d_old = d_new
|
||
d2_old = d2_new
|
||
psi_old = psi_new
|
||
|
||
return energy/normalization, N_accep/nmax
|
||
|
||
|
||
# Run simulation
|
||
a = 1.2
|
||
nmax = 100000
|
||
dt = 0.05
|
||
tau = 100.
|
||
E_ref = -0.5
|
||
|
||
X0 = [ MonteCarlo(a, nmax, dt, tau, E_ref) for i in range(30)]
|
||
|
||
# Energy
|
||
X = [ x for (x, _) in X0 ]
|
||
E, deltaE = ave_error(X)
|
||
print(f"E = {E} +/- {deltaE}")
|
||
|
||
# Acceptance rate
|
||
X = [ x for (_, x) in X0 ]
|
||
A, deltaA = ave_error(X)
|
||
print(f"A = {A} +/- {deltaA}")
|
||
#+END_SRC
|
||
|
||
#+RESULTS:
|
||
: E = -0.500188803288012 +/- 0.0010615739297642462
|
||
: A = 0.9896496666666668 +/- 7.688845979106312e-05 *Fortran*
|
||
#+BEGIN_SRC f90
|
||
subroutine pdmc(a, dt, nmax, energy, accep, tau, E_ref)
|
||
implicit none
|
||
double precision, intent(in) :: a, dt, tau
|
||
integer*8 , intent(in) :: nmax
|
||
double precision, intent(out) :: energy, accep
|
||
double precision, intent(in) :: E_ref
|
||
|
||
integer*8 :: istep
|
||
integer*8 :: n_accep
|
||
double precision :: sq_dt, chi(3), d2_old, prod, u
|
||
double precision :: psi_old, psi_new, d2_new, argexpo, q
|
||
double precision :: r_old(3), r_new(3)
|
||
double precision :: d_old(3), d_new(3)
|
||
double precision :: e, w, norm, tau_current
|
||
|
||
double precision, external :: e_loc, psi
|
||
|
||
sq_dt = dsqrt(dt)
|
||
|
||
! Initialization
|
||
energy = 0.d0
|
||
n_accep = 0_8
|
||
norm = 0.d0
|
||
|
||
w = 1.d0
|
||
tau_current = 0.d0
|
||
|
||
call random_gauss(r_old,3)
|
||
|
||
call drift(a,r_old,d_old)
|
||
d2_old = d_old(1)*d_old(1) + &
|
||
d_old(2)*d_old(2) + &
|
||
d_old(3)*d_old(3)
|
||
|
||
psi_old = psi(a,r_old)
|
||
|
||
do istep = 1,nmax
|
||
e = e_loc(a,r_old)
|
||
w = w * dexp(-dt*(e - E_ref))
|
||
|
||
norm = norm + w
|
||
energy = energy + w*e
|
||
|
||
tau_current = tau_current + dt
|
||
|
||
! Reset when tau is reached
|
||
if (tau_current > tau) then
|
||
w = 1.d0
|
||
tau_current = 0.d0
|
||
endif
|
||
|
||
call random_gauss(chi,3)
|
||
r_new(:) = r_old(:) + dt*d_old(:) + chi(:)*sq_dt
|
||
|
||
call drift(a,r_new,d_new)
|
||
d2_new = d_new(1)*d_new(1) + &
|
||
d_new(2)*d_new(2) + &
|
||
d_new(3)*d_new(3)
|
||
|
||
psi_new = psi(a,r_new)
|
||
|
||
! Metropolis
|
||
prod = (d_new(1) + d_old(1))*(r_new(1) - r_old(1)) + &
|
||
(d_new(2) + d_old(2))*(r_new(2) - r_old(2)) + &
|
||
(d_new(3) + d_old(3))*(r_new(3) - r_old(3))
|
||
|
||
argexpo = 0.5d0 * (d2_new - d2_old)*dt + prod
|
||
|
||
q = psi_new / psi_old
|
||
q = dexp(-argexpo) * q*q
|
||
|
||
call random_number(u)
|
||
|
||
if (u <= q) then
|
||
|
||
n_accep = n_accep + 1_8
|
||
|
||
r_old(:) = r_new(:)
|
||
d_old(:) = d_new(:)
|
||
d2_old = d2_new
|
||
psi_old = psi_new
|
||
|
||
end if
|
||
|
||
end do
|
||
|
||
energy = energy / norm
|
||
accep = dble(n_accep) / dble(nmax)
|
||
|
||
end subroutine pdmc
|
||
|
||
program qmc
|
||
implicit none
|
||
double precision, parameter :: a = 1.2d0
|
||
double precision, parameter :: dt = 0.05d0
|
||
double precision, parameter :: E_ref = -0.5d0
|
||
double precision, parameter :: tau = 100.d0
|
||
integer*8 , parameter :: nmax = 100000
|
||
integer , parameter :: nruns = 30
|
||
|
||
integer :: irun
|
||
double precision :: X(nruns), accep(nruns)
|
||
double precision :: ave, err
|
||
|
||
do irun=1,nruns
|
||
call pdmc(a, dt, nmax, X(irun), accep(irun), tau, E_ref)
|
||
enddo
|
||
|
||
call ave_error(X,nruns,ave,err)
|
||
print *, 'E = ', ave, '+/-', err
|
||
|
||
call ave_error(accep,nruns,ave,err)
|
||
print *, 'A = ', ave, '+/-', err
|
||
|
||
end program qmc
|
||
#+END_SRC
|
||
|
||
#+begin_src sh :results output :exports results
|
||
gfortran hydrogen.f90 qmc_stats.f90 pdmc.f90 -o pdmc
|
||
./pdmc
|
||
#+end_src
|
||
|
||
#+RESULTS:
|
||
: E = -0.49963953547336709 +/- 6.8755513992017491E-004
|
||
: A = 0.98963533333333342 +/- 6.3052128284666221E-005
|
||
|
||
|
||
|
||
* Project
|
||
|
||
Change your PDMC code for one of the following:
|
||
- the Helium atom, or
|
||
- the H_2 molecule at $R$ =1.401 bohr.
|
||
|
||
And compute the ground state energy.
|
||
|
||
|
||
|
||
* Schedule :noexport:
|
||
|
||
|------------------------------+---------|
|
||
| <2021-02-04 Thu 09:00-10:30> | Lecture |
|
||
|------------------------------+---------|
|
||
| <2021-02-04 Thu 11:00-11:20> | 2.1 |
|
||
| <2021-02-04 Thu 11:20-11:40> | 2.2 |
|
||
| <2021-02-04 Thu 11:40-12:15> | 2.3 |
|
||
| <2021-02-04 Thu 12:15-12:30> | 2.4 |
|
||
|------------------------------+---------|
|
||
| <2021-02-04 Thu 14:00-14:10> | 3.1 |
|
||
| <2021-02-04 Thu 14:10-14:30> | 3.2 |
|
||
| <2021-02-04 Thu 14:30-15:30> | 3.3 |
|
||
| <2021-02-04 Thu 15:30-16:30> | 3.4 |
|
||
| <2021-02-04 Thu 16:30-18:30> | 4.5 |
|
||
|------------------------------+---------|
|
||
|
||
* Acknowledgments
|
||
|
||
[[https://trex-coe.eu/sites/default/files/inline-images/euflag.jpg]]
|
||
|
||
[[https://trex-coe.eu][TREX]] : Targeting Real Chemical Accuracy at the Exascale project
|
||
has received funding from the European Union’s Horizon 2020 - Research and
|
||
Innovation program - under grant agreement no. 952165. The content of this
|
||
document does not represent the opinion of the European Union, and the European
|
||
Union is not responsible for any use that might be made of such content.
|
||
|