Documentation at: https://kliff.readthedocs.io
KLIFF is an interatomic potential fitting package that can be used to fit physics-motivated (PM) potentials, as well as machine learning potentials such as the neural network (NN) models.
conda intall -c conda-forge kliffpip install kliffgit clone https://github.com/openkim/kliff
pip install ./kliff
To train a KIM model, kim-api and kimpy are needed; to train a machine learning
model, PyTorch is needed. For more information on installing these packages, see
Installation.
from kliff import nn
from kliff.calculators import CalculatorTorch
from kliff.descriptors import SymmetryFunction
from kliff.dataset import Dataset
from kliff.models import NeuralNetwork
from kliff.loss import Loss
from kliff.utils import download_dataset
# Descriptor to featurize atomic configurations
descriptor = SymmetryFunction(
cut_name="cos", cut_dists={"Si-Si": 5.0}, hyperparams="set51", normalize=True
)
# Fully-connected neural network model with 2 hidden layers, each with 10 units
N1 = 10
N2 = 10
model = NeuralNetwork(descriptor)
model.add_layers(
# first hidden layer
nn.Linear(descriptor.get_size(), N1),
nn.Tanh(),
# second hidden layer
nn.Linear(N1, N2),
nn.Tanh(),
# output layer
nn.Linear(N2, 1),
)
# Training set (dataset will be downloaded from:
# https://github.com/openkim/kliff/blob/master/examples/Si_training_set.tar.gz)
dataset_path = download_dataset(dataset_name="Si_training_set")
dataset_path = dataset_path.join("varying_alat")
train_set = Dataset(dataset_path)
configs = train_set.get_configs()
# Set up calculator to compute energy and forces for atomic configurations in the
# training set using the neural network model
calc = CalculatorTorch(model)
calc.create(configs)
# Define a loss function and train the model by minimizing the loss
loss = Loss(calc, residual_data={"forces_weight": 0.3})
result = loss.minimize(method="Adam", num_epochs=10, batch_size=100, lr=0.001)
# Write trained model as a KIM model to be used in other codes such as LAMMPS ans ASE
model.write_kim_model()Detailed explanation and more tutorial examples can be found in the documentation.
- Interacting seamlessly with KIM, the fitted model can
be readily used in simulation codes such as LAMMPS and ASE via the
KIM API - Creating mixed PM and NN models
- High level API, fitting with a few lines of codes
- Low level API for creating complex NN models
- Parallel execution
- PyTorch backend for NN
@Article{wen2021kliff,
title = {{KLIFF}: A framework to develop physics-based and machine learning interatomic potentials},
author = {Mingjian Wen and Yaser Afshar and Ryan S. Elliott and Ellad B. Tadmor},
journal = {arXiv preprint arXiv:2108.03523},
url = {https://arxiv.org/abs/2108.03523},
year = {2021},
}