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.

The following neural network architecture are included:

• simple CNNs and MLPs,
• ResNets,
• DenseNets

They can be applied on MNIST, CIFAR10 and CIFAR100.

Download/Clone the code using

git clone https://github.com/fdraxler/PyTorch-AutoNEB
cd PyTorch-AutoNEB

and run

python main.py project_directory config_file

where 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 configs/cifar10-resnet20.yaml.

Install the torch_autoneb package by running

python setup.py

in the root directory of this repository. You can then use it in Python via

import torch_autoneb