A CSFG-based neural network basis-selection method for large-scale RCI calculations within GRASPG
Abstract
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.
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BibTeX
@misc{shi2026csfgbased,
title = {{A CSFG-based neural network basis-selection method for large-scale RCI calculations within GRASPG}},
author = {Chaofan Shi and Shaowei Tian and Ran Si and Gediminas Gaigalas and Per Jönsson and Chongyang Chen},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.21202},
eprint = {2609.21202},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.21202},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - UNPB
TI - A CSFG-based neural network basis-selection method for large-scale RCI calculations within GRASPG
AU - Chaofan Shi
AU - Shaowei Tian
AU - Ran Si
AU - Gediminas Gaigalas
AU - Per Jönsson
AU - Chongyang Chen
PY - 2026
DA - 2026/09/18
PB - arXiv
DO - 10.48550/arxiv.2609.21202
UR - https://arxiv.org/abs/2609.21202
AB - 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.
ER -