提出新型神经网络波函数，大幅提升大规模电子结构计算速度与精度。
Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems
- 通过限制电子间相互作用范围，降低神经网络变分蒙特卡洛的复杂度
- 180个电子计算实现实际加速10倍，能量误差在化学精度内
- 适合需要高精度的大规模量子化学与材料设计研究
我们提出有限范围嵌入（FiRE），一种用于高精度大规模从头算电子结构计算的新波函数近似。相较于现有神经网络波函数，FiRE将神经网络变分蒙特卡洛（NN-VMC）的渐近复杂度降低了约n_el（电子数）。通过将电子-电子相互作用限制在神经网络内部，FiRE加速了采样、赝势和拉普拉斯计算等关键操作，在实际中实现了180电子计算的10倍加速。我们在多种挑战性体系上验证了方法的准确性，包括生物分子、共轭烃类和有机金属化合物。在这些体系中，FiRE的能量结果始终处于化学精度范围内，甚至在某些情况下优于高精度方法如CCSD(T)、AFQMC或当前的NN-VMC。凭借在计算效率和精度上的双重提升，FiRE成为快速且准确的大规模从头算计算新标准，有望推动量子化学、固体物理和材料设计领域的计算研究。
原文摘要 · Abstract (English)
We present finite-range embeddings (FiRE), a novel wave function ansatz for accurate large-scale ab-initio electronic structure calculations. Compared to contemporary neural-network wave functions, FiRE reduces the asymptotic complexity of neural-network variational Monte Carlo (NN-VMC) by $\sim n_\text{el}$, the number of electrons. By restricting electron-electron interactions within the neural network, FiRE accelerates all key operations -- sampling, pseudopotentials, and Laplacian computations -- resulting in a real-world $10\times$ acceleration in now-feasible 180-electron calculations. We validate our method's accuracy on various challenging systems, including biochemical compounds, conjugated hydrocarbons, and organometallic compounds. On these systems, FiRE's energies are consistently within chemical accuracy of the most reliable data, including experiments, even in cases where high-accuracy methods such as CCSD(T), AFQMC, or contemporary NN-VMC fall short. With these improvements in both runtime and accuracy, FiRE represents a new `gold-standard' method for fast and accurate large-scale ab-initio calculations, potentially enabling new computational studies in fields like quantum chemistry, solid-state physics, and material design.
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