Potential model¶

pinn.models.potential_model requires one dictionary as input params. The dictionary typically looks like this:

{'model_dir': '/path/to/model',
 'network': 'pinet',
 'network_params': {
     'atom_types':[1, 6, 7, 8, 9]
 },
 'model_params': {
  'learning_rate': 1e-3,
  'e_scale': 627.5,
  'e_dress': dress
 }
}

params¶

params contains the entire specification of the model, including the network and training setups.

model_dir is the directory of the estimator, where the training log and trained parameters are saved.
network could be string: the name an implemented network, or a defined network function.
network_params are the parameters which will be fed into the network function
model_params are the parameters that control the potential model. Below is a list of those parameters and their default values.

### Loss function options
'max_energy': False,     # if set to float, omit energies larger than it
'use_e_per_atom': False, # use e_per_atom to calculate e_loss
'use_e_per_sqrt': False, #
'log_e_per_atom': False, # log e_per_atom and its distribution
                         # ^- this is forcely done if use_e_per_atom
'use_e_weight': False,   # scales the loss according to e_weight
'use_force': False,      # include force in Loss function
'max_force': False,      # if set to float, omit forces larger than it
'use_f_weights': False,  # scales the loss according to f_weights
'use_l2': False,         # L2 regularization
### Loss function multipliers
'e_loss_multiplier': 1.0,
'f_loss_multiplier': 1.0,
'l2_loss_multiplier': 1.0,
### Optimizer related
'learning_rate': 3e-4,   # Learning rate
'use_norm_clip': True,   # see tf.clip_by_global_norm
'norm_clip': 0.01,       # see tf.clip_by_global_norm
'use_decay': True,       # Exponential decay
'decay_step':10000,      # every ? steps
'decay_rate':0.999,      # scale by ?

potential_model automatically saves params in a params.yml file during the creation of the estimator. The potential model can then be invoked using potential_model('/path/to/model/').

Potential model as a ASE calculator¶

A calculator can be created from a model as simple as:

from pinn.models import potential_model
from pinn.calculator import PiNN_calc
calc = PiNN_calc(potential_modle('/path/to/model/'))
calc.calculate(atoms)

Energy, forces and stress (with PBC) calculations are implemented for the ASE calculator.