MBAR¶

The MBAR estimator is a light wrapper around the reference implementation of MBAR from pymbar (pymbar.mbar.MBAR). As a generalization of BAR, it uses information from all sampled states to generate an estimate for the free energy difference between each state.

API Reference¶

class alchemlyb.estimators.MBAR(maximum_iterations=10000, relative_tolerance=1e-07, initial_f_k=None, method='hybr', verbose=False)¶

Multi-state Bennett acceptance ratio (MBAR).

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.
initial_f_k (np.ndarray, float, shape=(K), optional) – Set to the initial dimensionless free energies to use as a guess (default None, which sets all f_k = 0).
method (str, optional, default="hybr") – The optimization routine to use. This can be any of the methods available via scipy.optimize.minimize() or scipy.optimize.root().
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

theta_¶

The theta matrix.

Type: DataFrame

states_¶

Lambda states for which free energy differences were obtained.

Type: list

fit(u_nk)¶

Compute overlap matrix of reduced potentials using multi-state 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

property overlap_matrix¶

MBAR overlap matrix.

The estimated state overlap matrix \(O_{ij}\) is an estimate of the probability of observing a sample from state \(i\) in state \(j\).

The overlap_matrix is computed on-the-fly. Assign it to a variable if you plan to re-use it.