WLTM

class WLTM : public feasst::Bias

Begin with WangLandau and end with TransitionMatrix.[1] For the benefits of using WangLandau for initialization and TransitionMatrix for production convergence, see the Appendix of Shen and Siderius[2].

WLTM operates in the follow three stages.

1. WangLandau only. With CriteriaWriter, WLTM writes both WangLandau and TransitionMatrix. At this stage, the LnProbability from WangLandau appears as ln_prob. Because TransitionMatrix has not been used yet, the bias appears as all zeros with the header of ln_prob_tm.

2. When the WangLandau flatness reaches collect_flatness, the CollectrionMatrix begins to update with each trial move, but the bias is still based off of WangLandau.

3. When the WangLandau flatness reaches min_flatness, and the number of CollectionMatrix sweeps is greater than min_collect_sweeps, then the bias switches to TransitionMatrix. Writh CriteriaWriter, the TransitionMatrix LnProbability appears as ln_prob, while the WangLandau bias appears as ln_prob_wl.

References:

[1]

M. Scott Shell, Pablo G. Debenedetti, and Athanassios Z. Panagiotopoulos. An improved Monte Carlo method for direct calculation of the density of states. The Journal of Chemical Physics, 119(18):9406–9411, November 2003. doi:10.1063/1.1615966.

[2]

Vincent K. Shen and Daniel W. Siderius. Elucidating the effects of adsorbent flexibility on fluid adsorption using simple models and flat-histogram sampling methods. The Journal of Chemical Physics, 140(24):244106, June 2014. doi:10.1063/1.4884124.

Arguments

• WangLandau arguments.

• TransitionMatrix arguments.

• collect_flatness: Begin populating the CollectionMatrix when WangLandau has completed this many flatness checks. Note that populating the CollectionMatrix does not necessarily mean that the CollectionMatrix is used to compute the Bias.

• min_collect_sweeps: In addition to WangLandau::min_flatness, do not use TransitionMatrix as bias until it has this minimum number of sweeps. If -1, do nothing (default: -1).