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Electrostatic interactions in atomistic and machine-learned potentials for polar materials

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arxiv 2412.01642 v1 pith:25I4FYZ4 submitted 2024-12-02 cond-mat.mtrl-sci physics.comp-ph

Lorenzo Monacelli , Nicola Marzari This is my paper · ORCID
classification cond-mat.mtrl-sciphysics.comp-ph
keywords electrostaticinteractionslong-rangematerialsmodelpolarpotentialsatomistic
verification ladder T0 review T1 audit T2 compute T3 formal

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abstract

Long-range electrostatic interactions critically affect polar materials. However, state-of-the-art atomistic potentials, such as neural networks or Gaussian approximation potentials employed in large-scale simulations, often neglect the role of these long-range electrostatic interactions. This study introduces a novel model derived from first principles to evaluate the contribution of long-range electrostatic interactions to total energies, forces, and stresses. The model is designed to integrate seamlessly with existing short-range force fields without further first-principles calculations or retraining. The approach relies solely on physical observables, like the dielectric tensor and Born effective charges, that can be consistently calculated from first principles. We demonstrate that the model reproduces critical features, such as the LO-TO splitting and the long-wavelength phonon dispersions of polar materials, with benchmark results on the cubic phase of barium titanate (BaTiO$_3$).

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Machine Learning the Energetics of Electrified Solid/Liquid Interfaces

    cond-mat.mtrl-sci 2025-05 conditional novelty 7.0 of 10

    RAZOR machine-learns the work function and Born charges of electrified interfaces, enabling bias-dependent molecular dynamics that predicts a pH-driven OH adsorption site switch on Cu(100).

  2. A universal augmentation framework for long-range electrostatics in machine learning interatomic potentials

    physics.chem-ph 2025-07 conditional novelty 6.0 of 10

    LES augments short-range MLIPs with long-range electrostatics learned from energies and forces alone, improving accuracy and enabling Born effective charge and dipole prediction.

  3. Efficient local atomic cluster expansion for BaTiO$_3$ close to equilibrium

    cond-mat.mtrl-sci 2025-05 conditional novelty 6.0 of 10

    Short-range machine-learning potentials trained on DFT accurately reproduce BaTiO3 phase transitions, switching, and defects even without explicit long-range Coulomb terms.

  4. Consistency between the Green-Kubo formula and Lorentz model for predicting the infrared dielectric function of polar materials

    cond-mat.mes-hall 2025-04 conditional novelty 6.0 of 10

    Green-Kubo and Lorentz-model infrared dielectric functions of MgO and LiH become consistent when the Lorentz model uses a frequency-dependent phonon self-energy from MD and electronic polarization is included via an ε...

  5. Learning charges and long-range interactions from energies and forces

    physics.comp-ph 2024-12 conditional novelty 6.0 of 10

    A latent-charge machine learning potential, trained only on energies and forces, recovers physical partial charges, dipoles, and quadrupoles and beats explicit-charge models on multiple benchmarks.

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