Large Electron Model: A Universal Ground State Predictor

2026-03-02

AI Summary

This work addresses the challenge of accurately predicting ground-state wave functions in strongly correlated electron systems by proposing a unified variational representation of many-body fermionic wave functions through a single neural network model based on the Fermi Sets architecture. By conditioning the network on Hamiltonian parameters and particle number, the method achieves simultaneous generalization across varying interaction strengths and system sizes—up to 50 particles—within a single model framework. Applied to two-dimensional harmonic traps, it predicts ground-state energies and real-space charge densities with high accuracy, significantly outperforming conventional density functional theory in the strongly correlated regime. This approach establishes a new foundational paradigm for variational-principle-based materials discovery.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Search and Optimization: Metareasoning and Metaheuristics
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
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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 $50$ 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.

Problem

• ground state prediction
• strong electron correlation
• variational wavefunction
• many-body fermionic systems
• Hamiltonian parameter manifold

Innovation

• Large Electron Model
• Fermi Sets
• variational wavefunction
• strong electron correlation
• foundation model