Foundation Neural-Network Quantum States for Molecular Potential Energy Surfaces in Second Quantization
Second-quantized neural-network quantum states have achieved accurate molecular energies, but extending them across molecular geometries requires a shared representation of the geometry-dependent wavefunction coefficients. We introduce geometry-conditioned foundation neural-network quantum states for molecular electronic structure in second quantization. A single autoregressive model learns a family of ground states from sparse anchor geometries and provides wavefunctions at untrained geometries without further optimization. Orbital alignment matches orbital identities and transports their phases, establishing an aligned orbital basis across geometries. Frozen energies reach chemical accu...
| Authors | Lizhong Fu, Jianan Wei, Wenguan Wang et al. |
|---|---|
| Primary category | physics.chem-ph |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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