article Machine Learning Potentials

AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs

Dylan M. Anstine, Roman Zubatyuk, Olexandr Isayev

Chemical Science Vol. 16 (23) pp. 10228–10244 2025 47 citations
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Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

Abstract

Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.

Keywords

neural network

Cite This Paper

@article{Anstine2025b,
  author = {Anstine, Dylan M. and Zubatyuk, Roman and Isayev, Olexandr},
  title = {AIMNet2: a neural network potential to meet your neutral, charged, organic, and elemental-organic needs},
  year = {2025},
  journal = {Chemical Science},
  volume = {16},
  number = {23},
  pages = {10228--10244},
  doi = {10.1039/d4sc08572h},
  url = {http://dx.doi.org/10.1039/D4SC08572H},
  publisher = {Royal Society of Chemistry (RSC)},
  keywords = {neural network},
  researchAreas = {ml-potentials},
  highlight = {Machine learned interatomic potentials (MLIPs) are reshaping computational chemistry practices because of their ability to drastically exceed the accuracy-length/time scale tradeoff.},
  citations = {47}
}

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