Learning Dynamical Mean Field Theory

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SUMMARY

The discussion focuses on the application of Dynamical Mean Field Theory (DMFT) to transport problems in strongly correlated systems. A master's student seeks resources to learn DMFT thoroughly enough to write their own code, particularly for toy problems and non-equilibrium scenarios. Key resources mentioned include Kristjan Haule's website and DMFT code available through the ALPS project, which provide practical examples and downloadable scripts for reverse engineering.

PREREQUISITES
  • Understanding of the Hubbard model in quantum many-body theory.
  • Familiarity with programming in Python.
  • Basic knowledge of Dynamical Mean Field Theory (DMFT).
  • Experience with computational physics tools, specifically ALPS.
NEXT STEPS
  • Explore Kristjan Haule's website for DMFT resources and code examples.
  • Study the ALPS project documentation for DMFT implementations.
  • Learn Python programming techniques for scientific computing.
  • Research non-equilibrium problems in strongly correlated systems using DMFT.
USEFUL FOR

This discussion is beneficial for master's students, researchers in condensed matter physics, and programmers interested in implementing Dynamical Mean Field Theory for computational physics applications.

maverick280857
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Hi,

I'm a masters student trying to apply DMFT to problems involving transport in strongly correlated systems. I have a cursory understanding of the physics behind the Hubbard model, which is to say, I have spent some time with it in a quantum many body theory course. However, I now want to learn DMFT to the point of being able to write my own DMFT code to solve a few toy problems before trying to figure out how to use it for non-equilibrium problems.

Could someone point me to a gentle introduction to DMFT which helps me develop some simple programs as well. I'm unable to find friendly references on the internet.

Thanks in advance!
 
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There are a few codes you can find online, I think python scripts. You can probably reverse engineer them. I think Kristjan Haule's website has a few downloads, and there is also some DMFT code with the ALPS project.
 
Thanks indeed OhYoungLions! That website looks like a very useful resource.
 

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