Work

Spin-adapted neural network wavefunctions in real space

Year: 2026

Type: article

Abstract: Spin plays a fundamental role in understanding electronic structure, yet many real-space wavefunction methods fail to adequately consider it. We introduce the spin-adapted antisymmetrization method, a general procedure that enforces exact total spin symmetry for antisymmetric many-electron wavefunctions in real space. In the context of neural network-based quantum Monte Carlo, our method leverages the expressiveness of deep neural networks to capture electron correlation while enforcing exact spin adaptation via group representation theory. This framework provides a principled route to embed physical priors into otherwise black-box neural network wavefunctions, yielding a compact representation of correlated systems with neural network orbitals. To demonstrate its effectiveness, we study the spin ladder of iron-sulfur clusters, a long-standing challenge for many-body methods due to their dense spectrum of nearly degenerate spin states. Our results reveal accurate resolution of low-lying spin states and spin gaps in [Fe2S2] and [Fe4S4] clusters, offering insights into their electronic structures. In sum, these findings establish our framework as a practical route toward spin-adapted neural network-based quantum Monte Carlo for strongly correlated systems. The authors introduce a general framework that embeds exact spin symmetry directly into the neural network wavefunction, guaranteeing spin-pure states by construction. This design enables accurate resolution of near-degenerate spin states in challenging chemical systems.

Source: Nature Communications

DOI: 10.1038/s41467-026-78600-y