Welcome to MultirefPredict’s documentation!¶
Automated workflow to predict multireference character of molecules in quantum chemistry calculation
Program Execution¶
A simple example of MultirefPredict’s capabilities of as follows:
>>> import MultirefPredict
>>> import qcelemental
>>> mol = qcelemental.models.Molecule.from_data("""
O 0.000000000000 0.000000000000 -0.068516245955
H 0.000000000000 -0.790689888800 0.543701278274
H 0.000000000000 0.790689888800 0.543701278274
""")
Here we just built a molecule object in qcelemental format. Various multireference diagnostics can be calculated for this molecule with the computeDiagnostic syntax of the diagnostic object, which is initialized with the diagnostic_factory function.
>>> b1 = MultirefPredict.diagnostic_factory("B1",molecule=mol, molname="water", record=False).computeDiagnostic()
b1 is the returned diagnostic value
>>> b1
0.006334860365228678
The I/O of the backend quantum chemistry package is totally hidden from the user. If one would like to get detailed information about the quantum chemistry calculation, it can be easily achieved by setting the record keyword to True.
>>> b1 = MultirefPredict.diagnostic_factory("B1",molecule=mol, molname="water", record=True).computeDiagnostic()
Then the jason files recording the quantum chemistry calculations associated with this diagnostic calculation are automatically dumped in the working directory
>>> ls
B1_water_b1lyp_H.json
B1_water_b1lyp_O.json
B1_water_b1lyp_whole.json
B1_water_blyp_H.json
B1_water_blyp_O.json
B1_water_blyp_whole.json
These files are in the qcelemental result format. For example:
>>> with open('B1_water_b1lyp_H.json') as f:
data = json.load(f)
print(json.dumps(data,indent=4))
{
"molecule": {
"symbols": [
"H"
],
"geometry": [
0.0,
0.0,
0.0
],
"molecular_charge": 0.0,
"molecular_multiplicity": 2
},
"driver": "energy",
"model": {
"method": "b1lyp",
"basis": "6-31g"
},
...
}