citation.cff

cff-version: 1.2.0
message: If you use this software, please cite it as below.
title: CHGNet as a pretrained universal neural network potential for charge-informed atomistic modelling
authors:
  - family-names: Deng
    given-names: Bowen
  - family-names: Zhong
    given-names: Peichen
  - family-names: Jun
    given-names: KyuJung
  - family-names: Riebesell
    given-names: Janosh
  - family-names: Han
    given-names: Kevin
  - family-names: Bartel
    given-names: Christopher J.
  - family-names: Ceder
    given-names: Gerbrand
date-released: 2023-02-24
repository-code: https://github.com/CederGroupHub/chgnet
arxiv: https://arxiv.org/abs/2302.14231
doi: 10.1038/s42256-023-00716-3
url: https://chgnet.lbl.gov
type: software
keywords:
  [machine learning, neural network, potential, charge, molecular dynamics]
version: 0.2.0 # replace with the version you use
journal: Nature Machine Intelligence