A Fixed-Point Neural Operator for Size- and Functional-Transferable Hamiltonian Prediction
Predicting the Kohn-Sham Hamiltonian with machine learning can accelerate density functional theory while retaining access to molecular orbitals, energy levels, and electronic-structure observables that energy-only surrogates cannot resolve. Yet element-wise agreement with the converged Hamiltonian, an implicit fixed point of the self-consistent field iteration, does not determine the occupied subspace that governs orbital energies and densities. Here we present HamEvo, a neural operator that learns the single-step self-consistent update and returns the converged Hamiltonian as its fixed point. HamEvo is pre-trained on intermediate self-consistent trajectories and calibrated at equilibriu...
| Authors | Yunhong Lou, Xihang Yue, Xinran Wei et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 12 June 2026 |
| Last updated at source | 11 August 2026 |
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