Large Electron Model: A Universal Ground State Predictor
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Abstract (arXiv)
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. For 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 50 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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 6 of its 36 references that are in the map. First posted on arXiv 2 Mar 2026.
Builds on
6 of 36 references.
References · 6
- 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
- 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 45
- 2021Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functionscited by 36
- 2023Generalizing Neural Wave Functionscited by 26
- 2022Sampling-free Inference for Ab-Initio Potential Energy Surface Networkscited by 19
- 2025An ab initio foundation model of wavefunctions that accurately describes chemical bond breakingcited by 11
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Citing papers per year: 2026: 3.
3 papers.