This framework implements NEB (Henkelman and Jónsson, 2000) and AutoNEB (Kolsbjerg, Groves and Hammer, 2016) in PyTorch. It efficiently finds low energy paths between minima of arbitrary loss/energy functions.
This framework was developed to be applied to neural networks, but is truely generic to any (Auto)NEB+Python application. Several examples for neural network architectures are given.
Implemented models/loss functions
The following neural network architecture are included:
- simple CNNs and MLPs,
They can be applied on MNIST, CIFAR10 and CIFAR100.
Setup your environment, e.g. using
conda install pyyaml conda install pytorch torchvision -c pytorch
Optional, but recommended: Install
tqdm top geht progress bars while running:
conda install tqdm
Download/Clone the code using
git clone https://github.com/fdraxler/PyTorch-AutoNEB cd PyTorch-AutoNEB
Running the examples
python main.py project_directory config_file
project_directory is the directory (need not exist) where the data should be stored.
config_file should point to one of the
.yaml files in configs.
You can create new config files by editing an existing, such as
Use in your own code
torch_autoneb package by running
in the root directory of this repository. You can then use it in Python via