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.

A more robust version of MBAR is provided as AutoMBAR, where the class will iteratively try different means of solving the MBAR estimate to avoid unconverged results. The process of iterating different methods is documented in the logger alchemlyb.estimators.AutoMBAR.

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 from pymbar is desired. Output from alchemlyb is logged via logging.

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

See also

pymbar.MBAR

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.

See also

pymbar.mbar.MBAR.computeOverlap

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

class alchemlyb.estimators.AutoMBAR(maximum_iterations=10000, relative_tolerance=1e-07, initial_f_k=None, verbose=False)

A more robust version of Multi-state Bennett acceptance ratio (MBAR).

Given that there isn’t a single method that would allow MBAR to converge for every single use case, the AutoMBAR estimator iteratively tries all the available methods to obtain the converged estimate.

The fastest method hybr will be tried first, followed by the most stable method adaptive. If adaptive does not converge, BFGS will be used as last resort. Although BFGS is not as stable as adaptive, it has been shown to succeed in some cases where adaptive cannot.

AutoMBAR may be useful in high-throughput calculations where it can avoid failures due non-converged MBAR estimates.

Note

All arguments are described under MBAR except that the solver method is determined by AutoMBAR as described above.

See also

MBAR

New in version 0.6.0.