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arXiv Machine Learning By Qiao Gao, Yang Huang, Enze Hou, Yuzhi Liu, Jingrun Chen, Han Wang

NucleoNet: A Nuclear-Aware Determinant Ansatz for Multi-Geometry Neural Variational Monte Carlo

Read the original on arXiv Machine Learning →

NucleoNet is a neural‑network variational Monte Carlo ansatz that treats electrons and nuclei as a unified particle set, allowing a single model to be optimized across multiple geometries. The approach uses a shared electron–nucleus backbone to evolve one‑particle and two‑particle representations, with nuclear streams learning representations that condition orbital construction while antisymmetry is enforced only by electronic determinants. On small‑molecule benchmarks, NucleoNet matches the accuracy of established neural‑wavefunction baselines with far fewer parameters and successfully describes dissociation curves and minimum‑energy paths for several systems, demonstrating that a compact wavefunction can capture molecular geometries with a single set of parameters.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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