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Models in PiNN

For regression problems for an atomic or molecular property, it's often sufficient to use an network. However, atomic machine learning tasks it's often desired to train on properties derived from the atomic predictions, such as forces, stress tensors for dipole moments. pinn.models are created for defining these tasks in a network-agnostic way.

Two models are implemented in PiNN at this point, their respective options can be found in the "Implemented models" section.

Configuration

Models implemented in PiNN used a serialized format for their parameters. The parameter file specifies the network architecture, hyperparameters and training algorithm. A typical parameter file include the following sections:

model_dir: pinet_potential
model:
  name: potential_model
  params:
    use_force: true
network:
  name: PiNet
  params:
    atom_types: [1, 6, 7, 8, 9]
optimizer:
  class_name: EKF
  config:
    learning_rate: 0.03

Among those, the optimizer section follows the format of a Keras optimizer. The model and network sections specify the name and parameters of initialize a PiNN model and network, respectively. A model can be initialized by a parameter file or a corresponding nested python dictionary.

Training

The model maybe created by calling the corresponding model function, and a parameter dictionary mirroring the parameter file:

import yaml
from pinn.models.potential import potential_model
with open('params.yml') as f:
    params = yaml.load(f, Loader=yaml.Loader)
model = potential_model(params)

PiNN provides a shortcut pinn.get_model to create an implemented model from a parameter dictionary or parameter file.

model = pinn.get_model('params.yml')

pinn.get_model automatically saves a copy params.yml file in the model directory. When such a file exist, the model can be loaded with its directory as well.

model = pinn.get_model('pinet_potential')

The PiNN model is a TensorFlow estimator, to train the model in a python script:

filelist = glob('/home/yunqi/datasets/QM9/dsgdb9nsd/*.xyz')
dataset = lambda: load_qm9(filelist, splits={'train':8, 'test':2})
train = lambda: dataset()['train'].repeat().shuffle(1000).apply(sparse_batch(100))
test = lambda: dataset()['test'].repeat().apply(sparse_batch(100))
train_spec = tf.estimator.TrainSpec(input_fn=train, max_steps=1000)
eval_spec = tf.estimator.EvalSpec(input_fn=test, steps=100)
tf.estimator.train_and_evaluate(model, train_spec, eval_spec)

ASE interface

PiNN provides a PiNN_calc class to interface models with ASE. A calculator can be created from a model as simple as:

calc = pinn.get_calc('pinet_potential')
calc.calculate(atoms)

The implemented properties of the calculator depend on the model. For example: the potential model implements energy, forces and stress (with PBC) calculations and the dipole model implements partial charge and dipole calculations.