pyiron.gaussian.gaussian module

class pyiron.gaussian.gaussian.Gaussian(project, job_name)

Bases: pyiron.dft.job.generic.GenericDFTJob

bsse_to_pandas()

Convert bsse output of all frames to a pandas Dataframe object.

Returns

output as dataframe

Return type

pandas.Dataframe

calc_md(temperature=None, n_ionic_steps=1000, time_step=None, n_print=100)

calc_minimize(electronic_steps=None, ionic_steps=None, algorithm=None, ionic_forces=None)

Function to setup the hamiltonian to perform ionic relaxations using DFT. The convergence goal can be set using either the iconic_energy as an limit for fluctuations in energy or the iconic_forces.

Arguments

algorithm: SCF algorithm electronic_steps (int): maximum number of electronic steps per electronic convergence ionic_steps (int): maximum number of ionic steps ionic_forces (‘tight’ or ‘verytight’): convergence criterium for Berny opt (optional)

calc_static(electronic_steps=None, algorithm=None)

Function to setup the hamiltonian to perform static SCF DFT runs

Arguments

algorithm (str): SCF algorithm electronic_steps (int): maximum number of electronic steps, which can be used to achieve convergence

collect_output()

Collect the output files of the external executable and store the information in the HDF5 file. This method has to be implemented in the individual hamiltonians.

from_hdf(hdf=None, group_name=None)

Recreates instance from the hdf5 file :param hdf: Path to the hdf5 file :type hdf: str :param group_name: Name of the group which contains the object :type group_name: str

log()

print_MO()

Print a list of the MO’s with the corresponding orbital energy and occupation.

read_NMA()

Reads the NMA output from the Gaussian .log file.

Returns

IR frequencies, intensities and corresponding eigenvectors (modes).

to_hdf(hdf=None, group_name=None)

Store the GenericJob in an HDF5 file

Parameters

• hdf (ProjectHDFio) – HDF5 group object - optional

• group_name (str) – HDF5 subgroup name - optional

visualize_MO(index, particle_size=0.5, show_bonds=True)

Visualize the MO identified by its index.

Arguments

index index of the MO, as listed by print_MO()

particle_size

size of the atoms for visualization, lower value if orbital is too small to see

show_bonds connect atoms or not

Notes

This function should always be accompanied with the following commands (in a separate cell)

view[1].update_surface(isolevel=1, color=’blue’, opacity=.3) view[2].update_surface(isolevel=-1, color=’red’, opacity=.3)

This makes sure that the bonding and non-bonding MO’s are plotted and makes them transparent

write_input()

Write the input files for the external executable. This method has to be implemented in the individual hamiltonians.

class pyiron.gaussian.gaussian.GaussianInput(input_file_name=None)

Bases: pyiron.base.generic.parameters.GenericParameters

load_default()

Loading the default settings for the input file.

pyiron.gaussian.gaussian.collect_output(output_file)

pyiron.gaussian.gaussian.fchk2dict(fchk)

pyiron.gaussian.gaussian.get_bsse_array(line, it)

pyiron.gaussian.gaussian.read_EmpiricalDispersion(output_file, output_dict)

pyiron.gaussian.gaussian.read_bsse(output_file, output_dict)

pyiron.gaussian.gaussian.write_input(input_dict, working_directory='.')