Preparing dataset

SIMPLE-NN uses ASE to handle output from ab initio programs like VASP or Quantum ESPRESSO. All output types supported by ASE can be used in SIMPLE-NN, but they need to contain essential information such as atom coordinates, lattice parameters, energy, and forces. You can check if the output file contains the appropriate information by using the following command:

from ase import io

atoms = io.read('some_output')
# atom coordinates
# chemical symbols
# lattice parameters
# structure_energy
# atomic force
# structure_stress
atoms.get_stress() (need unit conversion!)

Preparing inputs files

SIMPLE-NN use YAML style input file: input.yaml. input.yaml consists of three part: basic parameters, feature-related parameters, and model-related parameters. The basic format of input.yaml is like below:

# Basic parameters
generate_features: true
preprocess: true
train_model: true
  - Si
  - O

# feature-related parameters
symmetry_function: # class name of the feature
  Si: params_Si
  O: params_O

# model-related parameters
neural_network: # class name of the model
  method: Adam
  nodes: 30-30
  batch_size: 10
  total_iteration: 50000
  learning_rate: 0.001

Depending on the feature and model classes, additional input files may be required. For example, for symmetry_function class, additional files named str_list and params_XX (name of params_XX can be changed) are required. Details of parameters and additional files are listed in /simple_nn/Simple_nn (basic parameters), Features (feature-related parameters) and Models (model-related parameters) section.

Running the code

To run SIMPLE-NN, you simply have to run the predefined script run.py after preparing all input files. The basic format of run.py is described below:

# run.py
# Usage:
# $ python run.py

from simple_nn import Simple_nn
from simple_nn.features.symmetry_function import Symmetry_function
from simple_nn.models.neural_network import Neural_network

model = Simple_nn('input.yaml',

Examples of actual use for the entire process of generating neural network potential can be found in Examples section or SIMPLE-NN/examples/


The default output file of SIMPLE-NN is LOG, which contains the execution log of SIMPLE-NN. In addition to LOG, an additional output file is created for each process of SIMPLE-NN. After Symmetry_function.generate method, you can find the output files listed below:

  • data/data##.pickle: (## indicates number) Data file which contains descriptor vectors, a derivative of descriptor vectors, and other parameters per structure.

After Symmetry_function.preprocess method, you can find the output files listed below:

  • data/{training,valid}_data_####_to_####.tfrecord: Packed Training/validation dataset which contains the information in data/data##.pickle and additional parameters like atomic weights which are used during training process.
  • pickle_{training,valid}_list: List of pickle files that are included in data/{training,valid}_data_####_to_####.tfrecord file.
  • {train,valid}_list: List of tfrecord files (used in network optimization process)
  • scale_factor: Scale factor for symmetry function.
  • atomic_weights: Data file that contains atomic weights.

After Neural_network.train method, you can find the output files listed below:

  • SAVER_iterationXXXX.*, checkpoint: Tensorflow save file which contains the network information at the XXXXth iteration.
  • potential_saved_iterationXXXX: LAMMPS potential file which contains the network information at the XXXXth iteration.

MD simulation with LAMMPS

To run MD simulation with LAMMPS, add the lines into the LAMMPS script file.

pair_style nn
pair_coeff * * /path/to/potential_saved Si O

Regarding the unit system, the NNP trained with VASP output is compatible with the LAMMPS units ‘metal’. For outputs from other ab initio programs, however, the appropriate unit should be chosen with the user’s discretion.