Optimized nested Markov chain Monte Carlo sampling: theory

Conference · Thu Jan 01 04:00:00 EST 2009

DOI:https://doi.org/10.1063/1.3295190· OSTI ID:989792

Coe, Joshua D ^[1]; Shaw, M Sam ^[1]; Sewell, Thomas D ^[2]

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1. Los Alamos National Laboratory
2. U. MISSOURI

Metropolis Monte Carlo sampling of a reference potential is used to build a Markov chain in the isothermal-isobaric ensemble. At the endpoints of the chain, the energy is reevaluated at a different level of approximation (the 'full' energy) and a composite move encompassing all of the intervening steps is accepted on the basis of a modified Metropolis criterion. By manipulating the thermodynamic variables characterizing the reference system we maximize the average acceptance probability of composite moves, lengthening significantly the random walk made between consecutive evaluations of the full energy at a fixed acceptance probability. This provides maximally decorrelated samples of the full potential, thereby lowering the total number required to build ensemble averages of a given variance. The efficiency of the method is illustrated using model potentials appropriate to molecular fluids at high pressure. Implications for ab initio or density functional theory (DFT) treatment are discussed.

• Research Organization: Los Alamos National Laboratory (LANL)
• Sponsoring Organization: DOE
• DOE Contract Number: AC52-06NA25396
• OSTI ID: 989792
• Report Number(s): LA-UR-09-03502; LA-UR-09-3502
• Country of Publication: United States
• Language: English

Related Subjects

75 CONDENSED MATTER PHYSICS
SUPERCONDUCTIVITY AND SUPERFLUIDITY
APPROXIMATIONS
CHAINS
EFFICIENCY
FUNCTIONALS
PROBABILITY
SAMPLING
THERMODYNAMICS