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:

[1]

Jayant K. Singh and David A. Kofke. Mayer Sampling: Calculation of Cluster Integrals using Free-Energy Perturbation Methods. Physical Review Letters, 92(22):220601, June 2004. doi:10.1103/PhysRevLett.92.220601.

[2]

Harold W. Hatch, Sally Jiao, Nathan A. Mahynski, Marco A. Blanco, and Vincent K. Shen. Communication: Predicting virial coefficients and alchemical transformations by extrapolating Mayer-sampling Monte Carlo simulations. The Journal of Chemical Physics, 147(23):231102, December 2017. doi:10.1063/1.5016165.

Public Functions

void precompute(System *system)

Disable optimization when overlap is detected.

bool is_accepted(const System &system, Acceptance *acceptance, Random *random)

Return whether or not the trial attempt should be accepted.

const Accumulator &mayer() const

Return the mayer ensemble of the full potential.

const Accumulator &mayer_ref() const

Return the mayer ensemble of the reference potential.

double second_virial_ratio() const

Return the ratio of the second virial coefficient of the full potential to the second virial coefficient of the reference potential.

double second_virial_ratio_block_stdev() const

Return the block standard deviation of the second_virial_ratio. This is computed using the error propogation formula variance formula:

\(f = mayer/mayer_ref\)

\(\sigma_f = \sqrt{(\sigma_{mayer}/mayer_ref)^2 + (f\sigma_{mayer_ref}/mayer_ref)^2}\)

int num_beta_taylor() const

Return the number of beta derivatives, starting with 1.

const std::vector<Accumulator> &beta_taylor() const

Return the beta derivatives in the mayer function f12.

double beta_taylor(const int deriv) const

Return the beta derivative in the second virial ratio by n factorial.

std::string write() const

Return a human-readable output of all data (not as brief as status).

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.