MayerSampling

class MayerSampling : public feasst::Criteria

Mayer-sampling Monte Carlo acceptance criteria as described in Singh and Kofke[1]. Extrapolation in temperature is performed as descirbed in Hatch et al.[2]. The coefficients for the Taylor series extrapolation are written to file and include the factorial division.

The RefPotential may need to be carefully adjusted when the tutorials are changed. Typically, the RefPotential is a HardSphere with a given ModelParams::sigma. For example, in <a href=”../tutorial/tutorial_4_cgmab.html”>this tutorial</a>, the size of the HardSphere RefPotential is given by “sigmaCOM=30 cutoffCOM=30”. Too small of a sigma leads to high error bars because the RefPotential will not often be sampled while sampling the Potential. Too large of a sigma means the RefPotential will not be fully sampled while sampling the Potential, and therefore gives incorrect results. A more optimal value of sigma typically results in a mayer_ref() of approximately 0.3 to 0.5.

References:

Arguments

  • trials_per_cycle: define a cycle as a number of trials (as measured by number of calls to is_accepted) default: 1e9.

  • intra_potential: index of intramolecular potential that will be used to select the move. Ignore if -1 (default: -1).

  • num_beta_taylor: number of derivatives of second virial ratio with respect to beta. (default: 0).

  • training_file: if not empty, file name to write training data (default: empty).

  • training_per_write: write every this many sets of data (default: 1e4).

  • Criteria arguments.