Examples

Query Example

This notebook will cover example usage of Optimization record. As a note we will be using the MolSSI QCArchive server as a data source. Any ids used in this example will not be valid for local servers.



In [1]:

    
import qcfractal.interface as ptl
client = ptl.FractalClient()
client









    Out[1]:





FractalClient

  Server:     The MolSSI QCArchive Server
  Address:    https://api.qcarchive.molssi.org:443/
  Username:   None

We can query results from the database based off a variety of values, but for this example we will query a known result from the database.



In [2]:

    
record = client.query_procedures(id=1683293)[0]
record









    Out[2]:





<OptimizationRecord(id='1683293' status='COMPLETE')>

There are a variety of helper functions on this object to find quantities related to the computation.



In [3]:

    
record.get_final_molecule()









    





        You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: 

        jupyter labextension install jupyterlab_3dmol
        







    Out[3]:





<Molecule(name='C14H14O' formula='C14H14O' hash='2594158')>



In [4]:

    
record.show_history()

We can also observe the program, method, and basis for which the optimization was executed under.



In [5]:

    
record.qc_spec.dict()









    Out[5]:





{'driver': <DriverEnum.gradient: 'gradient'>,
 'method': 'b3lyp-d3(bj)',
 'basis': 'dzvp',
 'keywords': None,
 'program': 'psi4'}

We can also find all keywords passed into the geometry optimization. Here we see that this geometry optimization was evaluated under a dihedral constraint.



In [6]:

    
record.keywords









    Out[6]:





{'coordsys': 'tric',
 'enforce': 0.1,
 'reset': True,
 'qccnv': True,
 'epsilon': 0,
 'constraints': {'set': [{'type': 'dihedral',
    'indices': [9, 10, 13, 14],
    'value': 30}]},
 'program': 'psi4'}

Finally, every Result generated in the computational trajectory can be queried and observed. Here we will obtain the very last computed Result.



In [7]:

    
record.get_trajectory()[-1]









    Out[7]:





<ResultRecord(id='1467860' status='COMPLETE')>

Compute Example

Computation of a result can be accomplished by specifying all parameters to a quantum chemistry computation and a molecule. An example can be seen below using a FractalSnowflake instance.



In [8]:

    
from qcfractal import FractalSnowflakeHandler
snowflake = FractalSnowflakeHandler()
client = snowflake.client()
client









    Out[8]:





FractalClient

  Server:     FractalSnowFlake_db_f1c89
  Address:    https://localhost:57605/
  Username:   None



In [9]:

    
methane = ptl.Molecule.from_data("pubchem:methane")
methane









    



	Searching PubChem database for methane (single best match returned)
	Found 1 result(s)






    





        You appear to be running in JupyterLab (or JavaScript failed to load for some other reason).  You need to install the 3dmol extension: 

        jupyter labextension install jupyterlab_3dmol
        







    Out[9]:





<Molecule(name='IUPAC methane' formula='CH4' hash='b4057dd')>

To run a optimization on this methane molecule we need to specify the full input as shown below. It should be noted that this function is also organized in such a way where the optimization of many molecules with the same level of theory is most efficient.



In [11]:

    
options = {
    "keywords": {'coordsys': 'tric'}, # Geometry optimization program options
    "qc_spec": {                      # Quantum chemistry specifications
        "driver": "gradient",
        "method": "HF",
        "basis": "sto-3g",
        "keywords": None,
        "program": "psi4"
    },
}
compute = client.add_procedure("optimization", "geometric", options, [methane])
compute









    Out[11]:





<ComputeResponse(nsubmitted=1 nexisting=0)>

The ids of the submitted optimization can then be queried and examined. As a note the computation is not instantaneous, you may need to wait a moment and requery for this small molecule.



In [15]:

    
result = client.query_procedures(id=compute.ids)[0]
result









    Out[15]:





<OptimizationRecord(id='1' status='COMPLETE')>



In [20]:

    
ch_bond_original = result.get_initial_molecule().measure([0, 1])
ch_bond_optimized = result.get_final_molecule().measure([0, 1])
print(f"Optimized/Original C-H bond {ch_bond_original}/{ch_bond_optimized} (bohr)")









    



Optimized/Original C-H bond 2.0639574742067777/2.0465935246983378 (bohr)



In [21]:

    
result.show_history()



In [ ]: