mlsanityData to 7 Oct 2026

Large Electron Model: A Universal Ground State Predictor

arXiv 2603.02346 ↗First posted 2 Mar 2026cond-mat.str-el cs.AI cs.LGcited by 3

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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.

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6 of 36 references.

References · 6

  1. 2019Ab-Initio Solution of the Many-Electron Schr"odinger Equation with Deep Neural Networkscited by 195
  2. 2022A Self-Attention Ansatz for Ab-initio Quantum Chemistrycited by 45
  3. 2021Ab-Initio Potential Energy Surfaces by Pairing GNNs with Neural Wave Functionscited by 36
  4. 2023Generalizing Neural Wave Functionscited by 26
  5. 2022Sampling-free Inference for Ab-Initio Potential Energy Surface Networkscited by 19
  6. 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.