Exclusions and nonbonded scaling (such as 1-2, 1-3, and 1-4 scaling) are handled in EEX using a nonbond scaling list. This list can be passed directly or built by setting 1-2, 1-3, 1-4 scaling.
Relevant functions are:
This function is used to directly set scaling parameters between atoms. scaling_df is a dataframe with mandatory columns atom_index1, atom_index2, that also contains at least one of the columns vdw_scale or coul_scale.
# Build scaling dataframe
scale_df = pd.DataFrame()
scale_df["coul_scale"] = [0.0, 0.0, 0.0]
scale_df["atom_index1"] = [1,1,2]
scale_df["atom_index2"] = [2,3,3]
scale_df["vdw_scale"] = [0.5, 0.5, 0.5]
# Add data to datalayer
dl.set_pair_scalings(scale_df)
This function is used to set scaling factors for all atoms in a bond, angle or dihedral.
Here, nb_scaling_factors is a dictionary of the form:
nb_scaling_factors = {
"coul":{
"scale12": "dimensionless",
"scale13": "dimensionless",
"scale14": "dimensionless",
},
"vdw":{
"scale12": "dimensionless",
"scale13": "dimensionless",
"scale14": "dimensionless",
}
}
This function uses the scaling factors set in set_nb_scaling_factors to build a full list of the same format as scaling_df in set_pair_scalings and stores it in the datalayer.
Default for nb_labels is [vdw_scale, coul_scale] if no argument is given. This returns a pandas DataFrame with columns atom_index1, atom_index2, coul_scale and vdw_scale.
Returns dictionary from dl.set_nb_scaling_factors function
In [1]:
import eex
In [ ]:
# Write examples here
# For now, see test_datalayer.py (test_nb_scaling and test_set_nb_scaling_factors)