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

TL;DR

This paper introduces a physics-informed method to incorporate long-range Coulomb interactions into machine-learning Hamiltonians, significantly improving accuracy and transferability for polar materials and heterostructures.

Contribution

The authors derive a closed-form, variationally consistent long-range Coulomb correction and implement it in a neural network architecture, enhancing the modeling of electrostatics in complex materials.

Findings

Achieves 2-3x error reduction in benchmark systems.

Eliminates staircase artifacts in electrostatic potential modeling.

Demonstrates robust transferability to larger and more complex systems.

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.