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Ch. 17.4 Schrodinger Equation

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Program 1

Schrodinger Equation

Python 3.11 Ready

                                
Click "Run Code" to execute script and render figures.

Mathematical Problem Formulation

Consider a one-dimensional quantum system described by the time-dependent Schrödinger equation.

\[ i\hbar \frac{\partial \psi(x,t)}{\partial t} = \left( -\frac{\hbar^2}{2m} \frac{\partial^2}{\partial x^2} + V(x) \right) \psi(x,t) \]

Time evolution of a wave packet moving in free space. A Gaussian wave packet is defined as:

$$\psi(x, t=0) = A \cdot e^{-\frac{(x - x_0)^2}{2 \sigma^2}} \cdot e^{i k_0 x}$$

The energy of the wave packet is defined as:

$$E = \frac{\hbar^2 k_0^2}{2m} + \frac{\hbar^2}{2m \sigma^2}$$

Analyze how this energy influences the time evolution of the wave packet.

Program 2

Schrodinger Equation

Python 3.11 Ready

                                
Click "Run Code" to execute script and render figures.

Mathematical Problem Formulation

Simulating Time Evolution of a Gaussian Wave Packet in a Potential Well

Consider a one-dimensional system where a quantum particle is evolving in time under the influence of a potential barrier. The time evolution of the wave function \( \psi(x, t) \) is governed by the time-dependent Schrödinger equation:

\[ i\hbar \frac{\partial \psi(x,t)}{\partial t} = \left( -\frac{\hbar^2}{2m} \frac{\partial^2}{\partial x^2} + V(x) \right) \psi(x,t) \]

take: \( m = 1.0 \) and \( \hbar = 1.0 \)

The initial wave function is a Gaussian wave packet centered at \( x_0 = 100 \) with width \( \sigma = 10 \) and wavenumber \( k_0 = \frac{\pi}{10} \). The corresponding initial wave function is:

\[ \psi(x, 0) = A \exp\left( -\frac{(x - x_0)^2}{2\sigma^2} \right) e^{ik_0 x} \]

where \( A \) is a normalization constant.

The barrier height is given by \( V_0 = 0.1 \).

The simulation is performed over \( N = 1000 \) spatial points and up to \( T = 10,000 \) time steps.

The energy of the wave packet is given by:

\[ E = \frac{\hbar^2}{2m} \left( k_0^2 + \frac{1}{2\sigma^2} \right) \]

Python program simulates the time evolution of the wave packet using finite difference methods, and plot the real and imaginary parts of the wave function \( \psi(x, t) \) as well as the probability density \( |\psi(x,t)|^2 \) at various time steps.

Theoretical Principles & Computational Physics Guide

Schrodinger Equation: Differential Equations & Dynamical Simulation

Dr. Alorika Chatterjee & Dr. Anirban Shaw Science & Physics Learners Python 3 / NumPy / SciPy

1. Core Principles & Theoretical Framework

Dynamical Evolution: Solves differential equations governing kinematics, planetary orbits, electrical circuits, and wave propagation.
Step-by-Step Stepping: Advances continuous state vectors forward in discrete time intervals $\Delta t$ using slope estimates.
Conservation Laws: Evaluates preservation of physical invariants (total energy, angular momentum) across long integration horizons.

2. Mathematical Formulation & Algorithmic Pipeline

Vector initial value problem and numerical state advancement:

$$ \frac{d\mathbf{y}}{dt} = \mathbf{f}(t, \mathbf{y}), \quad \mathbf{y}(t_0) = \mathbf{y}_0 \implies \mathbf{y}_{n+1} = \mathbf{y}_n + \Delta t \cdot \mathbf{\Phi}(t_n, \mathbf{y}_n, \Delta t) $$

3. Numerical Mechanics & Implementation Logic

  • Coupled Reduction: Convert $n$-th order ODEs into systems of $n$ coupled first-order equations.
  • Intermediate Slopes: Compute weighted intermediate derivatives ($k_1, k_2, k_3, k_4$ in RK4).
  • State Update: Advance state variables simultaneously and verify energy/phase invariants.

4. Relevant Computational Practice Exercises

Exercise 1
Solve the differential equation for Schrodinger Equation over 1000 time steps and plot state coordinates vs time.
Exercise 2
Compare numerical solution stability for small vs large step sizes $h = \Delta t$.
Exercise 3
Plot phase space trajectory demonstrating physical stability or damping attractors.
Standard Literature & References:
  • John R. Taylor, "Classical Mechanics", University Science Books (2005).
  • Mark Newman, "Computational Physics", University of Michigan (2013).