Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians

• URL: http://arxiv.org/abs/2603.20007v1
• Date: Fri, 20 Mar 2026 14:51:36 GMT
• Title: Physics-Informed Long-Range Coulomb Correction for Machine-learning Hamiltonians
• Abstract summary: Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory.<n>We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis.<n>We implement this framework in HamGNN-LR, a dual-channel architecture combining E(3)-equivariant message passing with reciprocal-space Ewald summation.
• Score: 10.42921523649481
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine-learning electronic Hamiltonians achieve orders-of-magnitude speedups over density-functional theory, yet current models omit long-range Coulomb interactions that govern physics in polar crystals and heterostructures. We derive closed-form long-range Hamiltonian matrix elements in a nonorthogonal atomic-orbital basis through variational decomposition of the electrostatic energy, deriving a variationally consistent mapping from the electron density matrix to effective atomic charges. We implement this framework in HamGNN-LR, a dual-channel architecture combining E(3)-equivariant message passing with reciprocal-space Ewald summation. Benchmarks demonstrate that physics-based long-range corrections are essential: purely data-driven attention mechanisms fail to capture macroscopic electrostatic potentials. Benchmarks on polar ZnO slabs, CdSe/ZnS heterostructures, and GaN/AlN superlattices show two- to threefold error reductions and robust transferability to systems far beyond training sizes, eliminating the characteristic staircase artifacts that plague short-range models in the presence of built-in electric fields.

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