发表机构
Institute of Modern Physics, Fudan University; Institute of Theoretical Physics and Astronomy, Vilnius University; Department of Materials Science and Applied Mathematics, Malmö University（复旦大学现代物理研究所; 维尔纽斯大学理论与天体物理研究所; 马尔默大学材料科学与应用数学系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一种基于CSFG的神经网络基组选择方法，用于GRASPG大规模RCI计算，大幅减少学习单元和输入维度，在保持精度的同时显著降低计算时间和内存开销。
AI 中文摘要
我们提出了一种用于GRASPG中大规模相对论组态相互作用（RCI）计算的神经网络（NN）基组选择方法。该方法采用组态态函数生成器（CSFG）作为神经网络的基本选择单元，每个CSFG生成一组具有相同自旋-角动量耦合的组态态函数（CSF）。一种恒定轨道特征消除策略移除了在CSFG池中数值保持不变的通道。CSFG表示将神经网络处理的学习单元数量减少了一个数量级以上，而恒定轨道特征消除进一步降低了神经网络输入的维度。结合高性能的GRASPG框架，该方法提高了神经网络选择和后续RCI计算的效率，在精度与计算成本之间保持了平衡。在一个中等规模的Ni(12+)基准测试中，相应的全空间RCI计算仍然可行，由保留的CSFG集合生成的CSF在目标态上以几波数（inverse-centimeter）的精度重现了全空间RCI结果。对于具有代表性的J=0、偶宇称块，完整工作流程将墙钟时间减少了75.6%，单次RCI计算所需的峰值内存减少了10.1倍。在一个包含1.27×10^9个CSF的全CSF展开的大规模计算中，该方法仅保留了全空间CSF的1.1%至1.9%，所得能级与实验数据及其他资源密集型理论计算结果高度一致。
英文摘要
We present a neural network (NN) basis-selection method for large-scale relativistic configuration interaction (RCI) calculations in GRASPG. The method employs configuration state function generators (CSFGs), each of which generates a set of configuration state functions (CSFs) with the same spin-angular couplings, as the basic selection units for the NN. A constant-orbital feature-elimination strategy removes feature channels whose values remain unchanged across the CSFG pool. The CSFG representation reduces the number of learning units processed by the NN by more than one order of magnitude, while constant-orbital feature elimination further reduces the dimensionality of the NN input. Combined with the high-performance GRASPG framework, the method improves the efficiency of both NN selection and subsequent RCI calculations, maintaining a balance between accuracy and computational cost. In a moderate Ni(12+) benchmark, where the corresponding full-space RCI calculation is still feasible, the CSFs generated by the retained CSFG sets reproduce the full-space RCI results at the few inverse-centimeter level for the target states. For the representative J = 0, even-parity block, the complete workflow reduces the wall time by 75.6 percent, and the peak memory required by a single RCI calculation is reduced by a factor of 10.1. In a larger-scale calculation with a full CSF expansion containing 1.27 x 10^9 CSFs, the method retains only 1.1-1.9 percent of the full-space CSFs and yields energy levels in good agreement with experimental data and other resource-intensive theoretical calculations.
Comments27 pages, 2 figures, and 6 tables. Submitted to Computer Physics Communications
