# Valid keywords

Keyword | Description | Type | Default |
---|---|---|---|

`use_ml` |
use a machine learning model to predict the screening parameters | `bool` |
`False` |

`n_max` |
The maximum expansion coefficient n for radial basis functions. If a list is provided in the convergence_ml-task, a grid search will be performed | `int` /`list` |
`4` |

`l_max` |
The maximum angular expansion coefficient. If a list is provided in the convergence_ml-task, a grid search will be performed | `int` /`list` |
`4` |

`r_min` |
The width of the narrowest radial basis function. If a list is provided in the convergence_ml-task, a grid search will be performed | `float` /`list` |
`0.5` |

`r_max` |
The width of the broadest radial basis function. If a list is provided in the convergence_ml-task, a grid search will be performed | `float` /`list` |
`4.0` |

`criterium` |
The criterium which has to be satisfied in order to use the ML-predicted screening coefficients instead of computing them ab-initio | `str` |
`after_fixed_num_of_snapshots` (must be `after_fixed_num_of_snapshots` ) |

`number_of_training_snapshots` |
Number of snapshots needed for the "after_fixed_num_of_snapshots"-criterium. In case of the convergence_ml task, this number is taken to be the highest number of training samples for the convergence analysis | `int` |
`1` |

`current_snapshot` |
Number of snapshots already trained on | `int` |
`0` |

`alphas_from_file` |
If true, read the screening coefficients from file instead of calculating them ab-initio. The files have to be provided in the snapshot_ folders | `bool` |
`False` |

`train_on_the_fly` |
If true, the ML-model gets trained after the calculation of each orbital. If false, the ML-model gets trained at the end of each snapshot | `bool` |
`False` |

`occ_and_emp_together` |
If true, there will be one ML model for both occupied and empty states. If False, there will be one ML Model for occupied states and one for empty states | `bool` |
`True` |

`type_of_ml_model` |
Which ML model to use for making the predictions | `str` |
`ridge_regression` (must be `ridge_regression` /`linear_regression` /`mean` ) |

`input_data_for_ml_model` |
Which data to use in case of the ridge_regression or the linear-regression Model | `str` |
`orbital_density` (must be `orbital_density` /`self_hartree` ) |

`quantities_of_interest` |
Which quantities are we interested in the convergence_ml-task. Note that the eigenvalues (evs) require performing the final calculation afresh for every snapshot. | `str` /`list` |
`['alphas']` |