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Artificial intelligence-enhanced quantum chemical method with broad applicability

Author

Listed:
  • Peikun Zheng

    (Xiamen University)

  • Roman Zubatyuk

    (Carnegie Mellon University)

  • Wei Wu

    (Xiamen University)

  • Olexandr Isayev

    (Carnegie Mellon University)

  • Pavlo O. Dral

    (Xiamen University)

Abstract

High-level quantum mechanical (QM) calculations are indispensable for accurate explanation of natural phenomena on the atomistic level. Their staggering computational cost, however, poses great limitations, which luckily can be lifted to a great extent by exploiting advances in artificial intelligence (AI). Here we introduce the general-purpose, highly transferable artificial intelligence–quantum mechanical method 1 (AIQM1). It approaches the accuracy of the gold-standard coupled cluster QM method with high computational speed of the approximate low-level semiempirical QM methods for the neutral, closed-shell species in the ground state. AIQM1 can provide accurate ground-state energies for diverse organic compounds as well as geometries for even challenging systems such as large conjugated compounds (fullerene C60) close to experiment. This opens an opportunity to investigate chemical compounds with previously unattainable speed and accuracy as we demonstrate by determining geometries of polyyne molecules—the task difficult for both experiment and theory. Noteworthy, our method’s accuracy is also good for ions and excited-state properties, although the neural network part of AIQM1 was never fitted to these properties.

Suggested Citation

  • Peikun Zheng & Roman Zubatyuk & Wei Wu & Olexandr Isayev & Pavlo O. Dral, 2021. "Artificial intelligence-enhanced quantum chemical method with broad applicability," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-27340-2
    DOI: 10.1038/s41467-021-27340-2
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    References listed on IDEAS

    as
    1. Tsz Wai Ko & Jonas A. Finkler & Stefan Goedecker & Jörg Behler, 2021. "A fourth-generation high-dimensional neural network potential with accurate electrostatics including non-local charge transfer," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    2. Justin S. Smith & Benjamin T. Nebgen & Roman Zubatyuk & Nicholas Lubbers & Christian Devereux & Kipton Barros & Sergei Tretiak & Olexandr Isayev & Adrian E. Roitberg, 2019. "Approaching coupled cluster accuracy with a general-purpose neural network potential through transfer learning," Nature Communications, Nature, vol. 10(1), pages 1-8, December.
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