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PyTorch-AutoNEB

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,
  • ResNets,
  • DenseNets

They can be applied on MNIST, CIFAR10 and CIFAR100.

Installation

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

Usage

Running the examples

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.

Use in your own code

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

Results

The final MSTs for analysis with Evaluate.ipynb can be found at this repository. As of now, it contains only a subset of systems. Open an issue to request more systems.

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PyTorch AutoNEB implementation to identify minimum energy paths, e.g. in neural network loss landscapes

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