标题： 基于从头算的神经网络求解大规模电子结构问题的精确方法 显示英文标题

标题： Accurate Ab-initio Neural-network Solutions to Large-Scale Electronic Structure Problems

摘要： 我们提出了有限范围嵌入（FiRE），这是一种新颖的波函数近似方法，用于精确的大规模从头算电子结构计算。 与当代的神经网络波函数相比，FiRE通过减少神经网络变分蒙特卡洛（NN-VMC）的渐进复杂度，该复杂度由电子数量决定，具体为$\sim n_\text{el}$。 通过限制神经网络内的电子-电子相互作用，FiRE加速了所有关键操作——采样、伪势和拉普拉斯计算——从而在可行的180个电子计算中实现了实际的$10\times$加速。 我们在各种具有挑战性的系统上验证了我们方法的准确性，包括生物化学化合物、共轭烃和金属有机化合物。 在这类系统中，FiRE的能量始终在化学精度范围内，包括实验数据，即使在高精度方法如CCSD(T)、AFQMC或当代NN-VMC表现不佳的情况下也是如此。 凭借这些运行时间和准确性的改进，FiRE代表了一种新的“黄金标准”方法，用于快速而精确的大规模从头算计算，可能在量子化学、固态物理和材料设计等领域开启新的计算研究。 显示英文摘要

摘要： 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.