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  3. Qnep: A Highly Efficient Neuroevolution Potential With Dynamic Charges For Large-scale Atomistic Simulations.
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  3. Qnep: A Highly Efficient Neuroevolution Potential With Dynamic Charges For Large-scale Atomistic Simulations.

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qNEP: A Highly Efficient Neuroevolution Potential with Dynamic Charges for Large-Scale Atomistic Simulations.

Zheyong Fan1,2, Benrui Tang1, Esmée Berger3

  • 1College of Physical Science and Technology, Bohai University, Jinzhou 121013, P. R. China.

Journal of Chemical Theory and Computation
|April 20, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

We developed a charge-aware machine learning framework (qNEP) for efficient atomistic simulations of electrostatic phenomena. This method accurately captures dielectric properties and infrared spectra, enabling large-scale, long-time simulations.

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Area of Science:

  • Computational materials science
  • Machine learning in physics
  • Quantum chemistry

Background:

  • Traditional methods for simulating electrostatics are computationally expensive.
  • This limits large-scale, long-time simulations of phenomena driven by electrostatics.
  • Existing machine learning potentials often struggle to incorporate electrostatics efficiently.

Purpose of the Study:

  • To develop a computationally efficient machine learning framework for atomistic simulations incorporating electrostatics.
  • To enable accurate prediction of dielectric properties, infrared spectra, and field-matter coupling.
  • To extend the neuroevolution potential (NEP) to a charge-aware framework (qNEP).

Main Methods:

  • Introduced explicit, environment-dependent partial charges into the NEP framework, creating qNEP.
  • Represented each ionic partial charge using a neural network dependent on local descriptors.
  • Derived consistent expressions for forces and virials accounting for charge position dependence.
  • Implemented qNEP in the GPUMD package with Ewald summation and particle-particle particle-mesh support.

    • Main Results:

    • qNEP accurately predicts Born effective charge tensors and polarization.
    • Demonstrated accuracy and efficiency on water, Li7La3Zr2O12, BaTiO3, and a magnesium-water interface.
    • Achieved scalable simulations of million-atom systems on nanosecond timescales using GPUs.

    Conclusions:

    • qNEP enables accurate atomistic simulations with explicit long-range electrostatics.
    • The framework is suitable for studying dielectric response, infrared activity, and field-matter coupling.
    • qNEP offers a computationally efficient alternative for large-scale electrostatic simulations.