references.bib

@InProceedings{Zhang2018,
  author    = {Zhang, Linfeng and Han, Jiequn and Wang, Han and Saidi, Wissam A. and Car, Roberto and Weinan, E.},
  booktitle = {Proceedings of the 32nd {International} {Conference} on {Neural} {Information} {Processing} {Systems}},
  title     = {End-to-end symmetry preserving inter-atomic potential energy model for finite and extended systems},
  year      = {2018},
  address   = {Red Hook, NY, USA},
  pages     = {4441--4451},
  publisher = {Curran Associates Inc.},
  series    = {{NIPS}'18},
  abstract  = {Machine learning models are changing the paradigm of molecular modeling, which is a fundamental tool for material science, chemistry, and computational biology. Of particular interest is the inter-atomic potential energy surface (PES). Here we develop Deep Potential - Smooth Edition (DeepPot-SE), an end-to-end machine learning-based PES model, which is able to efficiently represent the PES of a wide variety of systems with the accuracy of ab initio quantum mechanics models. By construction, DeepPot-SE is extensive and continuously differentiable, scales linearly with system size, and preserves all the natural symmetries of the system. Further, we show that DeepPot-SE describes finite and extended systems including organic molecules, metals, semiconductors, and insulators with high fidelity.},
}

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