How to include disorder in the numerical computation?

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SUMMARY

The discussion focuses on incorporating disorder into numerical computations using the tight binding model, specifically addressing random site energy. It confirms that different realizations require distinct seeds in the random number generation subroutine to ensure variability. Additionally, it concludes that the final result should be calculated as the simple average of the realizations, represented mathematically as \(\frac{\sum_{n=1}^{100} P_n}{100}\). Monte Carlo algorithms are highlighted as a common method for integrating randomness in such calculations.

PREREQUISITES
  • Understanding of the tight binding model in quantum mechanics
  • Familiarity with random number generation techniques
  • Knowledge of Monte Carlo algorithms
  • Basic statistical concepts, particularly averaging
NEXT STEPS
  • Research Monte Carlo methods in computational physics
  • Explore random number generation libraries in Python, such as NumPy
  • Study the implementation of the tight binding model in numerical simulations
  • Learn about statistical analysis techniques for averaging results
USEFUL FOR

Physicists, computational scientists, and researchers involved in numerical modeling and simulations that require the incorporation of randomness and disorder in their calculations.

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For example, in tight binding model, the site energy is random
I want to have 100 disorder realizations, then I have the following
two questions:

1. Do different realization needs different seed in the random number generation subroutine?

2. When all realizations are completed, the result should be the simple average? (i.e., [tex]\frac{\sum_{n=1}^{100} P_n}{100}[/tex].
)
Please help me, if you know, Thank you in advance!
 
Last edited:
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Whenever one needs to include some "random" processes in a particular calculation or modeling, one tends to use some form of Monte Carlo algorithm. Not sure if this is applicable to you, but it is used a lot in different areas of physics.

Zz.
 
Thanks you, Zz, you are right.
 

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