TI¶

The TI estimator is a simple implementation of thermodynamic integration that uses the trapezoid rule for integrating the space between \(\left<\frac{dH}{d\lambda}\right>\) values for each \(\lambda\) sampled.

API Reference¶

class alchemlyb.estimators.TI(verbose=False)¶

Thermodynamic integration (TI).

Parameters: 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

dhdl¶

The estimated dhdl of each state.

Type: DataFrame

Changed in version 1.0.0: delta_f_, d_delta_f_, states_ are view of the original object.

fit(dHdl)¶

Compute free energy differences between each state by integrating dHdl across lambda values.

Parameters: dHdl (DataFrame) – dHdl[n,k] is the potential energy gradient with respect to lambda for each configuration n and lambda k.

separate_dhdl()¶

For transitions with multiple lambda, the attr:dhdl would return a DataFrame which gives the dHdl for all the lambda states, regardless of whether it is perturbed or not. This function creates a list of pandas.Series for each lambda, where each pandas.Series describes the potential energy gradient for the lambdas state that is perturbed.

Returns: dHdl_list – A list of pandas.Series such that dHdl_list[k] is the potential energy gradient with respect to lambda for each configuration that lambda k is perturbed.
Return type: list

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