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<p>Accurate prediction of molecular structure, energetics, and reactivity requires quantum chemistry methods that remain too costly for large-scale and high-throughput modeling. Semi-empirical tight-binding approaches are much faster, but their largely element-fixed parameters under-resolve local chemical-environment effects, limiting accuracy. We introduce a neural-network extension of extended-tight binding that learns bounded, environment-dependent shifts to Hamiltonian parameters, increasing chemical adaptivity while preserving self-consistency, charge and spin treatment, and correct long-range behavior. We show that, across benchmarks spanning main-group thermochemistry, noncovalent interactions, molecular forces, vibrational spectra and supramolecular complexes, this approach achieves density functional theory accuracy at near tight-binding cost. On the GMTKN55, the weighted mean absolute error decreases from 25.0 to 3.78 kcal/mol relative to GFN2-xTB; molecular force errors are lowest on eight of ten rMD17 molecules and improve by 10–40% relative to leading machine-learned interatomic potentials; and vibrational frequency errors decrease from 200.6 to 12.7 cm<sup>−1</sup>, with runtime increasing by less than 20%.</p>

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NN-xTB: density functional accuracy at semi empirical speed with neural network extended tight binding

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Yufan Xia,
  • Albert Thie,
  • Joshua Soon,
  • Giuseppe M. J. Barca

摘要

Accurate prediction of molecular structure, energetics, and reactivity requires quantum chemistry methods that remain too costly for large-scale and high-throughput modeling. Semi-empirical tight-binding approaches are much faster, but their largely element-fixed parameters under-resolve local chemical-environment effects, limiting accuracy. We introduce a neural-network extension of extended-tight binding that learns bounded, environment-dependent shifts to Hamiltonian parameters, increasing chemical adaptivity while preserving self-consistency, charge and spin treatment, and correct long-range behavior. We show that, across benchmarks spanning main-group thermochemistry, noncovalent interactions, molecular forces, vibrational spectra and supramolecular complexes, this approach achieves density functional theory accuracy at near tight-binding cost. On the GMTKN55, the weighted mean absolute error decreases from 25.0 to 3.78 kcal/mol relative to GFN2-xTB; molecular force errors are lowest on eight of ten rMD17 molecules and improve by 10–40% relative to leading machine-learned interatomic potentials; and vibrational frequency errors decrease from 200.6 to 12.7 cm−1, with runtime increasing by less than 20%.