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arXiv 2408.00477physics.atom-ph
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一种运行高精度原子计算的神经网络方法

A neural network approach to running high-precision atomic computations

Pavlo Bilous, Charles Cheung, Marianna Safronova

AI总结:

本文提出一种神经网络工具,通过迭代选择重要组态并与pCI程序集成,显著降低高精度原子组态相互作用计算的计算资源需求,并在Fe$^{16+}$和Ni$^{12+}$能级计算中得到验证。

摘要:

原子物理的现代应用,包括频率标准的确定和天体物理光谱的分析,要求以极高的精度预测原子性质。对于复杂原子系统,由于所涉及的电子组态集合呈指数级增长,高精度计算面临重大挑战。这加剧了这些计算所需计算资源的问题,使得开发从原本难以处理的海量集合中选取最重要组态的方法变得不可或缺。我们开发了一种神经网络(NN)工具,用于运行高精度原子组态相互作用(CI)计算,并迭代选择最重要的组态。该工具与成熟的pCI原子程序集成后,与没有神经网络支持的计算相比,我们的方法显著降低了计算资源需求。我们展示了若干由神经网络支持的Fe$^{16+}$和Ni$^{12+}$能级计算,并证明我们的方法可以可靠地使用并实现自动化,以解决各种系统的特定计算问题。

英文摘要:

Modern applications of atomic physics, including the determination of frequency standards, and the analysis of astrophysical spectra, require prediction of atomic properties with exquisite accuracy. For complex atomic systems, high-precision calculations are a major challenge due to the exponential scaling of the involved electronic configuration sets. This exacerbates the problem of required computational resources for these computations, and makes indispensable the development of approaches to select the most important configurations out of otherwise intractably huge sets. We have developed a neural network (NN) tool for running high-precision atomic configuration interaction (CI) computations with iterative selection of the most important configurations. Integrated with the established pCI atomic codes, our approach results in computations with significantly reduced computational requirements in comparison with those without NN support. We showcase a number of NN-supported computations for the energy levels of Fe$^{16+}$ and Ni$^{12+}$, and demonstrate that our approach can be reliably used and automated for solving specific computational problems for a wide variety of systems.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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