Quick Start

Below is a quick example to quickly make a force field for silicon.

from pyxtal_ff import PyXtal_FF

train_data = "pyxtal_ff/datasets/Si/PyXtal/Si8.json"
descriptors = {'type': 'SOAP',
               'Rc': 5.0,
               'parameters': {'lmax': 4, 'nmax': 3},
               'N_train': 400,
              }
model = {'system': ['Si'],
         'hiddenlayers': [16, 16],
        }
ff = PyXtal_FF(descriptors=descriptors, model=model)
ff.run(mode='train', TrainData=train_data)

The script will first compute the SOAP descriptor . As long as the descritors are obtained, they will be fed to the neural network trainining. Below is an example output from this quick script.

             ______       _    _          _         _______ _______
            (_____ \     \ \  / /        | |       (_______|_______)
             _____) )   _ \ \/ / |_  ____| |        _____   _____
            |  ____/ | | | )  (|  _)/ _  | |       |  ___) |  ___)
            | |    | |_| |/ /\ \ |_( ( | | |_______| |     | |
            |_|     \__  /_/  \_\___)_||_|_(_______)_|     |_|
                   (____/



================================= version 0.0.9 =================================

Descriptor parameters:
type        : SOAP
Rc          : 5.0
nmax        : 3
lmax        : 4


2012 structures have been loaded.
Computing the descriptors...
 400 out of  400
Saving descriptor-feature data to Si-SOAP/Train_db.dat

==================================== Training ====================================

No of structures   : 400
No of descriptors  : 30
No of parameters   : 785
No of epochs       : 1
Optimizer          : lbfgs
Force_coefficient  : 0.03
Stress_coefficient : None
Batch_size         : None

Iteration   99:
eng_loss:   0.020505     force_loss:   0.022794   stress_loss:   0.000000  regularization:   0.000000
    Loss:   0.043299     Energy MAE:     0.1383     Force MAE:     0.2759     Stress MAE:     0.0000

Iteration  100:
eng_loss:   0.020105     force_loss:   0.022543   stress_loss:   0.000000  regularization:   0.000000
    Loss:   0.042649     Energy MAE:     0.1380     Force MAE:     0.2756     Stress MAE:     0.0000

The training time: 116.85 s
The Neural Network Potential is exported to Si-SOAP/16-16-checkpoint.pth


============================= Evaluating Training Set ============================

The results for energy:
    Energy R2     0.993670
    Energy MAE    0.138006
    Energy RMSE   0.200526
The energy figure is exported to: Si-SOAP/Energy_Train.png


The results for force:
    Force R2      0.880971
    Force MAE     0.275650
    Force RMSE    0.707787
The force figure is exported to: Si-SOAP/Force_Train.png

After the training is complete, the optimized weight information will be stored as Si-SOAP/16-16-checkpoint.pth, where 16-16 describes the neuron information. In the meantime, the code also provide graphicial output to facilitate the analysis.

If you feel that the quality of results are not satisfactory, you can contine the training from the previous run file (Si-SOAP/16-16-checkpoint.pth) with the restart option.

from pyxtal_ff import PyXtal_FF

train_data = "pyxtal_ff/datasets/Si/PyXtal/Si8.json"
descriptors = {'type': 'SOAP',
               'Rc': 5.0,
               'parameters': {'lmax': 4, 'nmax': 3},
               'N_train': 400,
              }
model = {'system': ['Si'],
         'hiddenlayers': [16, 16],
         'restart': 'Si-SOAP/16-16-checkpoint.pth',
        }
ff = PyXtal_FF(descriptors=descriptors, model=model)
ff.run(mode='train', TrainData=train_data)

The results for energy:
    Energy R2     0.997013
    Energy MAE    0.093162
    Energy RMSE   0.137752
The energy figure is exported to: Si-SOAP/Energy_Train.png


The results for force:
    Force R2      0.951379
    Force MAE     0.200881
    Force RMSE    0.452365
The force figure is exported to: Si-SOAP/Force_Train.png

Clearly, running another 100 training steps notably reduces the MAE values. Thereforce, we can continue to train it by specifying the epoch option.

from pyxtal_ff import PyXtal_FF

train_data = "pyxtal_ff/datasets/Si/PyXtal/Si8.json"
descriptors = {'type': 'SOAP',
               'Rc': 5.0,
               'parameters': {'lmax': 4, 'nmax': 3},
               'N_train': 400,
              }
model = {'system': ['Si'],
         'hiddenlayers': [16, 16],
         'restart': 'Si-SOAP/16-16-checkpoint.pth',
         'epoch': 600,
        }
ff = PyXtal_FF(descriptors=descriptors, model=model)
ff.run(mode='train', TrainData=train_data)

Below are the results after 1000 steps of training.