FPLAT HISTOGRAM MONTE CARLO FOR LOW TEMPERATURE SIMULATIONS
The flat histogram Monte Carlo (FHMC) algorithm has been proposed as an ef-ficient sampling scheme for problems with a complex free energy landscape. Its successful implementation requires fast and stable determination of the sampling weight function which can be a challenge for simulation at low temperatures. We describe here a polynomial parameterization of the sampling weight function which allows one to perform noise filtering and extrapolation at the same time. Efficiency of the scheme as compared to Bergs original iterative formula is demonstrated on the two-dimensional compass model for d-orbital ordering.
LEI-HAN TANG
Department of Physics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
国际会议
The Joint Conference of ICCP6 and CCP2003(第6届国际计算伦理会议)
北京
英文
84-90
2004-05-23（万方平台首次上网日期，不代表论文的发表时间）