[Illinois] PHYS466 2013 Lecture 18: Smart MC

By David M. Ceperley

Department of Physics, University of Illinois at Urbana-Champaign, Urbana, IL

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Abstract


Bio

Professor Ceperley received his BS in physics from the University of Michigan in 1971 and his Ph.D. in physics from Cornell University in 1976. After one year at the University of Paris and a second postdoc at Rutgers University, he worked as a staff scientist at both Lawrence Berkeley and Lawrence Livermore National Laboratories. In 1987, he joined the Department of Physics at Illinois. Professor Ceperley is a staff scientist at the National Center for Supercomputing Applications at Illinois.

Professor Ceperley's work can be broadly classified into technical contributions to quantum Monte Carlo methods and contributions to our physical or formal understanding of quantum many-body systems. His most important contribution is his calculation of the energy of the electron gas, providing basic input for most numerical calculations of electronic structure. He was one of the pioneers in the development and application of path integral Monte Carlo methods for quantum systems at finite temperature, such as superfluid helium and hydrogen under extreme conditions.

Professor Ceperley is a Fellow of the American Physical Society and a member of the American Academy of Arts and Sciences. He was elected to the National Academy of Sciences in 2006.

Cite this work

Researchers should cite this work as follows:

  • David M. Ceperley (2013), "[Illinois] PHYS466 2013 Lecture 18: Smart MC," https://nanohub.org/resources/18194.

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Time

Location

University of Illinois, Urbana-Champaign, IL

Submitter

NanoBio Node, George Michael Daley

University of Illinois at Urbana-Champaign

Tags

[Illinois] PHYS 466 Lecture 18: Smart MC
  • Always measure acceptance ratio 1. Always measure acceptance rati… 0
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  • Variance of energy 2. Variance of energy 116.52128644146228
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  • Always measure acceptance ratio. 3. Always measure acceptance rati… 169.33753687742234
    00:00/00:00
  • Variance of energy 4. Variance of energy 193.01504020362123
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  • Optimizing the moves 5. Optimizing the moves 202.80835309770347
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  • Comparison of MC and MD: Which is better? 6. Comparison of MC and MD: Which… 551.02880777462838
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  • Menu of Moves 7. Menu of Moves 765.6139295424307
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  • Preferential Sampling 8. Preferential Sampling 1442.7161450801182
    00:00/00:00
  • Heat Bath 9. Heat Bath 1919.4893272401225
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  • Force-Bias MC 10. Force-Bias MC 1997.0920344767744
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  • Force bias 11. Force bias 2002.4225718748191
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