Sending report ...Path Integral Monte Carlo
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Usage Stats Overall Period: Updated 15 Oct, 2008 Users: 68 Jobs: 298 Avg. exec. time: 19 secs Reviews & Citations Google/IEEE Avg. Review: Citations: 0
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This tool is open source, according to this license.
Available Versions
- 1.2.2 (published)
- 1.2.1 (unpublished)
- 1.2 (unpublished)
| Version | 1.2.2 - published on 23 Jul, 2008 |
|---|---|
| Contributor(s) | John Shumway Arizona State University Matthew Gilbert University of Texas at Austin |
| At a glance | Tool Description |
| Screenshots | |
| Description | Path integral Monte Carlo (PIMC) simulates particles (often electrons and ions) by directly sampling the canonical partition function. In the path integral formulation of quantum statistical mechanics developed by Richard Feynman, particles get represented by closed imaginary-time trajectories of length h/kT. PIMC simulations are able to compute total energies, correlation functions, charge distribution, and linear response functions for thermal equilibrium. As in many quantum Monte Carlo methods, PIMC has efficient scaling with system size, often order N or N2. Our application, pi or app-pimc, is well suited for modeling conduction electrons in quantum dots, quantum wires, and quantum wells. We have also tested it for ab initio calculations, but at this point only hydrogen and helium atoms work well. The app-pimc tool is a low-level wrapper for our application that allows the user to input a simulation description in XML, run the simulation in 1, 2, or 3 dimensions, and view results of scalar estimators. While this is an expert interface, we provide demo input files for quick simulations of a free particle, simple harmonic oscillator, and a hydrogen atom. The code is open source, so users have the option of installing a local version of the program on their machines if that better suites their research. |
| Powered by | pi: Open-Source Path Integral QMC Open source path integral simulation program developed by the Shumway research group. |
| Sponsored by | Work supported by NSF Grant DMR 0239819. |
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| Cite this work | If you reference this work in a publication, please cite as follows:
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| Type | Tools |
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