PythonCompphys
qmsolve
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PythonCompphys | qmsolve | |
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2 | 32 | |
46 | 748 | |
- | 10.2% | |
1.0 | 0.0 | |
about 1 year ago | over 1 year ago | |
Jupyter Notebook | Python | |
GNU General Public License v3.0 only | BSD 3-clause "New" or "Revised" License |
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PythonCompphys
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Python scientific programming projects for physics
I have a repository targeted at computational physics with python: https://github.com/aromanro/PythonCompphys
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Computational Physics in python
aromanro/PythonCompphys: Some python workbooks with various topics from Computational Physics (github.com)
qmsolve
- QMsolve: A Python module for solving and visualizing the Schrödinger equation
- Anyone has a relatively "simple" program for solving 2D time-dependent Schrödinger equations?
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I recently started playing around with Schrödinger's equation. And after a lot of tinkering I got a simulation to work! My first experiment: an electron versus a wall.
Very nice. I just stumbled upon this repo in my free time that I was going to take a look at https://github.com/quantum-visualizations/qmsolve. Also does visualization of Schrodinger’s eqn
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Quantum resonant tunneling simulation. Despite having less energy than the lower, the upper electron has a higher chance of passing through the barriers by exciting the resonant eigenstate of the nanostructure!
In the visualization, the color hue shows the phase of the wave function of the electron ψ(x,y, t), while the opacity shows the amplitude. The transmittance spectrum, computed by taking the Fourier transform of the incident and transmitted wavefunction, can be found in this plot.
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Visualization of the quantum energy eigenstates of an electron confined in a pumpkin-shaped potential immersed in a uniform, strong magnetic field
The simulation was done with qmsolve, a python open-source page we made for visualizing and solving the Schrödinger equation.
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Quantum mechanical simulation of the cyclotron motion of an electron confined under a strong, uniform magnetic field, made by solving the Schrödinger equation. As time passes, the wavepacket spatial distribution disperses until it finally reaches a stationary state with a fixed radial length!
As mentioned in another comment, here's the link to the source code. For the split-step method I found this resource very helpful. For the Crank-Nicholson method I don't have any free resources to share, except for the wikipedia article. There is another method from this article which is easier to implement than split-step or Crank-Nicholson since it doesn't require taking Fourier transforms or solving a system of equations. You may find the stability conditions to be too limiting when it comes to performance, at least when compared to the other methods.
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Simulation of a particle scattering in a Sierpinski carpets potential fractals (Schrödinger equation version). When the Sierpinski order is enough large (level 3) to make the separation of the blocks smaller than the particle wavelength, it is unable to penetrate it.
The simulator used can be found here.
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Quantum particles scattering in Sierpinski carpets potential fractals, made solving the Schrödinger equation [OC]
The Schrödinger equation was solved using a Split Fourier method, which is one of the most efficient and accurate methods to solve the time-dependent version. The simulator used is also OC and is posted here.
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Quantum Physics with Python: A Package for Solving and Visualizing the Schrödinger Equation
Also, the script that returns this visualization can be found here.
What are some alternatives?
ElectronVisualized - Public Archive: Beautiful and Elegant Quantum Mechanics Visualization.
SchrodingerWellPython - 2D 3D Time independent FDM Schrodinger equation solver for arbitrary shape of well
python-physics-stuffs - physics and science projects in python
QuTiP - QuTiP: Quantum Toolbox in Python
3D-printed-mirror-array - 3D-printable hexagonal mirror array capable of reflecting sunlight into arbitrary patterns
catsim - Computerized Adaptive Testing Simulator
notebooks - Tutorials, Research, Concepts and ideas notes
chaos-theory - Playing around with chaos theory simulations. Creating equilibrium graphs and visualizing the logistic maps.
quantumcomputingsim - A library to simulate quantum computations
benford_py - Python implementation of Benford's Law tests.