English

Large Electron Model: A Universal Ground State Predictor

Strongly Correlated Electrons 2026-03-04 v1 Artificial Intelligence Machine Learning

Abstract

We introduce Large Electron Model, a single neural network model that produces variational wavefunctions of interacting electrons over the entire Hamiltonian parameter manifold. Our model employs the Fermi Sets architecture, a universal representation of many-body fermionic wavefunctions, which is further conditioned on Hamiltonian parameter and particle number. On interacting electrons in a two-dimensional harmonic potential, a single trained model accurately predicts the ground state wavefunction while generalizing across unseen coupling strengths and particle-number sectors, producing both accurate real-space charge densities and ground state energies, even up to 5050 particles. Our results establish a foundation model method for material discovery that is grounded in the variational principle, while accurately treating strong electron correlation beyond the capacity of density functional theory.

Keywords

Cite

@article{arxiv.2603.02346,
  title  = {Large Electron Model: A Universal Ground State Predictor},
  author = {Timothy Zaklama and Max Geier and Liang Fu},
  journal= {arXiv preprint arXiv:2603.02346},
  year   = {2026}
}

Comments

8+5 pages, 5+4 figures, 1+1 tables

R2 v1 2026-07-01T10:59:58.104Z