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NequIP is an open-source code for building E(3)-equivariant interatomic potentials.

Documentation Status


PLEASE NOTE: the NequIP code is under active development and is still in beta versions 0.x.x. In general changes to the patch version (the third number) indicate backward compatible beta releases, but please be aware that file formats and APIs may change. Bug reports are also welcomed in the GitHub issues!


NequIP requires:

  • Python >= 3.7
  • PyTorch == 1.11.* or 1.13.* or later (do not use 1.12). (Some users have observed silent issues with PyTorch 2+, as reported in #311. Please report any similar issues you encounter.) PyTorch can be installed following the instructions from their documentation. Note that neither torchvision nor torchaudio, included in the default install command, are needed for NequIP.

You must install PyTorch before installing NequIP, however it is not marked as a dependency of nequip to prevent pip from trying to overwrite your PyTorch installation.

To install:

  • We use Weights&Biases (or TensorBoard) to keep track of experiments. This is not a strict requirement — you can use our package without it — but it may make your life easier. If you want to use it, create an account here and install the Python package:

    pip install wandb
  • Install NequIP

    NequIP can be installed from PyPI:

    pip install nequip

    or directly from source:

    git clone
    cd nequip
    pip install . 

Installation Issues

The easiest way to check if your installation is working is to train a toy model:

$ nequip-train configs/minimal.yaml

If you suspect something is wrong, encounter errors, or just want to confirm that everything is in working order, you can also run the unit tests:

pip install pytest
pytest tests/unit/

To run the full tests, including a set of longer/more intensive integration tests, run:

pytest tests/

If a GPU is present, the unit tests will use it.


The best way to learn how to use NequIP is through the Colab Tutorial. This will run entirely on Google's cloud virtual machine; you do not need to install or run anything locally.


! PLEASE NOTE: the first few calls to a NequIP model can be painfully slow. This is expected behaviour as the profile-guided optimization of TorchScript models takes a number of calls to warm up before optimizing the model. (The nequip-benchmark script accounts for this.)

Basic network training

To train a network, you run nequip-train with a YAML config file that describes your data set, model hyperparameters, and training options.

$ nequip-train configs/example.yaml

A number of example configuration files are provided:

  • configs/minimal.yaml: A minimal example of training a toy model on force data.
  • configs/minimal_eng.yaml: The same, but for a toy model that predicts and trains on only energy labels.
  • configs/example.yaml: Training a more realistic model on forces and energies. Start here for real models!
  • configs/full.yaml: A complete configuration file containing all available options along with documenting comments. This file is for reference, example.yaml is the right starting point for a project.

Training runs can also be restarted by running the same nequip-train command if the append: True option is specified in the original YAML. (Otherwise, a new training run with a different name can be started from the loaded state of the previous run.)

All nequip-* commands accept the --help option to show their call signatures and options.

Evaluating trained models (and their error)

The nequip-evaluate command can be used to evaluate a trained model on a specified dataset, optionally computing error metrics or writing the results to an XYZ file for further processing.

The simplest command is:

$ nequip-evaluate --train-dir /path/to/training/session/

which will evaluate the original training error metrics over any part of the original dataset not used in the training or validation sets.

For more details on this command, please run nequip-evaluate --help.

Deploying models

The nequip-deploy command is used to deploy the result of a training session into a model that can be stored and used for inference. It compiles a NequIP model trained in Python to TorchScript. The result is an optimized model file that has no dependency on the nequip Python library, or even on Python itself:

nequip-deploy build --train-dir path/to/training/session/ where/to/put/deployed_model.pth

For more details on this command, please run nequip-deploy --help.

Using models in Python

An ASE calculator is also provided in nequip.dynamics.

LAMMPS Integration

NequIP is integrated with the popular Molecular Dynamics code LAMMPS which allows for MD simulations over large time- and length-scales and gives users access to the full suite of LAMMPS features.

The interface is implemented as pair_style nequip. Using it requires two simple steps:

  1. Deploy a trained NequIP model, as discussed above.
nequip-deploy build path/to/training/session/ path/to/deployed.pth

The result is an optimized model file that has no Python dependency and can be used by standalone C++ programs such as LAMMPS.

  1. Change the LAMMPS input file to the nequip pair_style and point it to the deployed NequIP model:
pair_style	nequip
pair_coeff	* * deployed.pth <NequIP type for LAMMPS type 1> <NequIP type for LAMMPS type 2> ...

For installation instructions, please see the pair_nequip repository.

Plugins / extending nequip

nequip is a modular framework and extension packages can provide new model components, architectures, etc. The main extension package(s) currently available are:

  • Allegro: implements the highly parallelizable Allegro model architecture.

Details on writing and using plugins can be found in the Allegro tutorial and in nequip-example-extension.

References & citing

The theory behind NequIP is described in our preprint (1). NequIP's backend builds on e3nn, a general framework for building E(3)-equivariant neural networks (2). If you use this repository in your work, please consider citing NequIP (1) and e3nn (3):



NequIP is being developed by:

  • Simon Batzner
  • Albert Musaelian
  • Lixin Sun
  • Anders Johansson
  • Mario Geiger
  • Tess Smidt

under the guidance of Boris Kozinsky at Harvard.

Contact, questions, and contributing

If you find a bug or have a proposal for a feature, please post it in the Issues. If you have a question, topic, or issue that isn't obviously one of those, try our GitHub Discussions.

If you want to contribute to the code, please read

We can also be reached at albym[dot]seas[dt]harvard[dot].edu.