发表机构
University of Science and Technology of China; Zhejiang University（中国科学技术大学; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出几何条件化的基础神经网络量子态，通过自回归模型和轨道对齐，在二次量子化下高效预测分子势能面，达到化学精度并大幅降低计算成本。
AI 中文摘要
二次量子化的神经网络量子态已实现精确的分子能量，但将其推广到不同分子几何构型需要共享依赖于几何构型的波函数系数表示。我们提出了用于二次量子化分子电子结构的基础神经网络量子态，该量子态由几何构型条件决定。一个自回归模型从稀疏的锚定几何构型中学习基态族，并在未经训练的几何构型下无需进一步优化即可提供波函数。轨道对齐匹配轨道身份并传输其相位，从而在不同几何构型间建立对齐的轨道基。对于N$_2$、CO和H$_4$，冻结能量在每个未经训练的查询几何构型下均达到化学精度。在额外的分子路径上，经能量训练的波函数无需属性标签即可产生偶极矩、四极矩和自然占据数。在三个配对的N$_2$训练种子中，轨道对齐将未经训练的查询几何构型的平均绝对能量误差从34-37 mHa降低至0.049-0.085 mHa。在约1 mHa的平均绝对误差下，冻结评估相对于独立优化将每几何构型的成本降低了$986\ imes$，在161点N$_2$网格上估计端到端GPU成本降低$25.8\ imes$。
英文摘要
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 accuracy at every untrained query geometry for N$_2$, CO, and H$_4$. On additional molecular paths, the energy-trained wavefunctions yield dipoles, quadrupoles, and natural occupations without property labels. Across three paired N$_2$ training seeds, orbital alignment lowers the mean absolute energy error over all untrained query geometries from 34-37 mHa to 0.049-0.085 mHa. At approximately 1 mHa mean absolute error, frozen evaluation reduces the per-geometry cost by $986\times$ relative to independent optimization, yielding an estimated $25.8\times$ end-to-end GPU-cost reduction on a 161-point N$_2$ grid.
Comments23 pages, 7 figures
