NUCLEAR TECHNIQUES, Volume. 48, Issue 5, 050012(2025)

Machine learning methods for studying heavy-ion fusion cross sections

Zhilong LI1,2, Yongjia WANG2、*, and Qingfeng LI1,2、**
Author Affiliations
  • 1China Institute of Atomic Energy, Beijing 102413, China
  • 2School of Science, Huzhou University, Huzhou 313000, China
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    References(62)

    [9] MA Yugang, ZHAO Hongwei. High current heavy ion accelerator and its scientific research topic Editor's note[J]. Scientia Sinica (Physica, Mechanica & Astronomica), 50, 5(2020).

    [46] Ke G L, Meng Q, Finley T, Guyon I, Luxburg U V, Bengio S, ed et al. Highly efficient gradient boosting decision tree[C], 30, 3149-3157(2017).

    [59] Thampi A. Interpretable AI: Building explainable machine learning systems[M]. Simon and Schuster(2022).

    [60] Lundberg S. A unified approach to interpreting model predictions[J/OL]. arXiv preprint(2017).

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    Zhilong LI, Yongjia WANG, Qingfeng LI. Machine learning methods for studying heavy-ion fusion cross sections[J]. NUCLEAR TECHNIQUES, 2025, 48(5): 050012

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    Paper Information

    Category: Special Topics on Applications of Machine Learning in Nuclear Physics and Nuclear Data

    Received: Mar. 24, 2025

    Accepted: --

    Published Online: Jun. 26, 2025

    The Author Email: Yongjia WANG (王永佳), Qingfeng LI (李庆峰)

    DOI:10.11889/j.0253-3219.2025.hjs.48.250132

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