TESC (Technologies for Extreme Scale Computing) group
LA-SiGMA TESC (Technologies for Extreme Scale Computing) group meets every week on Thursday at 12:00PM -1:00PM (CDT). If you are in LSU you may attend locally at DMC 1034. You may also participate remotely via Adobe Connect (http://connect.lsu.edu/tesc/).
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Visit our wiki page at https://wiki.cct.lsu.edu/la-sigma/Documents.
|Hirsch-Fye QMC||In quantum Monte Carlo (QMC) simulations, a path integral is typically used to recast the quantum problem in D space dimensions onto a classical problem in D+1 space-time dimensions. One of the first successful applications of QMC was to study the problem of a correlated impurity in an uncorrelated host, e.g., Ce impurities in a metal. The Hirsch-Fye QMC (HFQMC) algorithm was developed for this problem. More recently, together with similar methods such as continuous time QMC (CTQMC), it has gained broader acceptance as the impurity or cluster solver for dynamical mean field theory and its cluster extensions such as the dynamical cluster approximation (DCA). There is a large community of users of HFQMC and CTQMC.|
|Variational QMC||Variational approaches have proven to be a useful tool to investigate strongly correlated systems Variational Monte Carlo (VMC) is an approximation to the correlated many-particle ground state wavefunction which depends on a number of adjustable parameters. Minimizing the total energy of the system with respect to these parameters yields an upper bound to the total energy of the system. The properties of the system can then be studied by evaluating the expectation values of various observables in this optimized ground state. The energy minimization problem can be cast as a Monte Carlo simulation by recognizing that the gradient of the energy with respect to the parameters is an expectation value that can be determined via a Monte Carlo calculation.|
|Parallel Tempering||Many important systems are characterized by ``energy landscapes" featuring many local minima separated by large energy barriers. As such, these systems present a tremendous challenge to molecular dynamics simulations that attempt to follow directly the system's time evolution. Monte Carlo can bypass this problem by stochastically moving directly from one local minimum to another; however, at temperatures very low compared to the energy barriers between minima, the method becomes inefficient. This occurs in protein folding and dockin, glassy systems, and frustrated magnetic systems. To avoid these problems, various methods have been proposed. One of the most widely used methods, first proposed by Charles Geyer in 1991, is to simulate a range of the temperatures and attempt to swap the samples at different temperatures. This is often referred as the replica exchange method or parallel tempering method. Parallel tempering is now widely used to improve the efficiency of Monte Carlo calculations. Examples include polymers and protein simulations, molecular dynamics simulations, spin glass simulations, electronic structure calculations, and quantum Monte Carlo for strongly correlated systems.|
|Molecular Dynamics and Ab Inito Codes||Current interests including benchmarking and collaborating in development of MD and ab initio codes like LAMMPS, NAMD, WIEN2K etc.|
|Sign Learning Kink based Quantum Monte Carlo||The Sign Learning Kink (SiLK) based Quantum Monte Carlo (QMC) method is based on Feynman's path integral formulation of quantum mechanics, and can reduce the minus sign problem when calculating energies in atomic and molecular systems.|
|GPU-docking||GPU accelerated Monte Carlo simulator of replica exchange protein docking algorithms.|
Please contact Wei P Feinstein, wfeinstein -AT- lsu.edu and Jian Tao jtao -AT- cct.lsu.edu, if you are interested in any activities of the group.
Get the latest info by subscribing to the
TESC Mailing list , or visit the TESC wiki page.