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
-
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.
-
get_params(deep=True)¶ Get parameters for this estimator.
- Parameters
deep (boolean, optional) – 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
mapping of string to any
-
set_params(**params)¶ Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form
<component>__<parameter>so that it’s possible to update each component of a nested object.- Returns
- Return type
self