BAR¶

The BAR estimator is a light wrapper around the implementation of the Bennett Acceptance Ratio (BAR) method from pymbar (pymbar.mbar.BAR). It uses information from neighboring sampled states to generate an estimate for the free energy difference between these state.

API Reference¶

class alchemlyb.estimators.BAR(maximum_iterations=10000, relative_tolerance=1e-07, method='false-position', verbose=False)¶

Bennett acceptance ratio (BAR).

Parameters

maximum_iterations (int, optional) – Set to limit the maximum number of iterations performed.
relative_tolerance (float, optional) – Set to determine the relative tolerance convergence criteria.
method (str, optional, default='false-position') – choice of method to solve BAR nonlinear equations, one of ‘self-consistent-iteration’ or ‘false-position’ (default: ‘false-position’)
verbose (bool, optional) – Set to True if verbose debug output is desired.

delta_f_¶

The estimated dimensionless free energy difference between each state.

Type: DataFrame

d_delta_f_¶

The estimated statistical uncertainty (one standard deviation) in dimensionless free energy differences.

Type: DataFrame

states_¶

Lambda states for which free energy differences were obtained.

Type: list

fit(u_nk)¶

Compute overlap matrix of reduced potentials using Bennett acceptance ratio.

Parameters: u_nk (DataFrame) – u_nk[n,k] is the reduced potential energy of uncorrelated configuration n evaluated at state k.

get_params(deep=True)¶

Get parameters for this estimator.

Parameters: deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.
Returns: params – Parameter names mapped to their values.
Return type: dict

set_params(**params)¶

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters: **params (dict) – Estimator parameters.
Returns: self – Estimator instance.
Return type: estimator instance