-
Abstract Nuclear masses are fundamental observables that reflect nuclear structure and stability, playing a key role in nuclear physics and astrophysical processes. Most existing neural network studies focus on predicting either binding energies or neutron/proton separation energies individually, with limited attention to the physical correlations between these observables. Based on the relativistic point-coupling model PCF-PK1, a physics-informed artificial neural network (ANN) was developed to systematically predict nuclear binding energies along with single- and double-neutron/proton separation energies, while preserving the physical self-consistency of the predictions. To assess the impact of incorporating separation-energy constraints, networks were trained with varying loss function weight combinations, enabling a comparison between networks without separation-energy constraints (e.g., ANN1) and those including such constraints (e.g., ANN3).
The neural network significantly improves the overall prediction accuracy of binding energies compared with the PCF-PK1 model. Without separation-energy constraints, ANN1 already achieves high precision for binding energies (RMSE ≈ 0.147 MeV) and separation energies (RMSE ≈ 0.158– 0.185 MeV). Incorporating separation-energy constraints in ANN3 results in a slight improvement in overall prediction accuracy. The binding energy predictions improve by approximately 4.6%, while the separation energy predictions increase by 8.9–12.0%. The improvement is particularly noticeable for nuclei where the deviations of ANN1 predictions from experimental values exceed 0.2 MeV. Supporting datasets are publicly accessible at the Science Data Bank (https://doi.org/10.57760/sciencedb.j00213.00239). To facilitate the review process, a private access link is provided for reviewers during the review period (https://www.scidb.cn/s/bqyemq).-
Keywords:
- Nuclear mass /
- Neural network /
- Separation energy
-
[1] Lunney D, Pearson J M, Thibault C 2003 Rev. Mod. Phys. 75 1021.
[2] Bender M, Heenen P H, Reinhard P G 2003 Rev. Mod. Phys. 75 121.
[3] Burbidge E M, Burbidge G R, Fowler W A, Hoyle F 1957 Rev. Mod. Phys. 29 547.
[4] Bethe H A 1939 Phys. Rev. 55 434.
[5] Huang W J, Wang M, Kondev F G, Audi G, Naimi S 2021 Chin. Phys. C 45 030002.
[6] Wang M, Huang W J, Kondev F G, Audi G, Naimi S 2021 Chin. Phys. C 45 030003.
[7] Xia X W, LiM Y, Zhao P W, Liang H Z, Qu X Y, Chen Y, Liu H, Zhang L F, Zhang S Q, Kim Y, Meng J 2018 At. Data Nucl. Data Tables 121 1.
[8] Bethe H A, Bacher R F 1936 Nucl. Phys. 8 82.
[9] Möller P, Myers W D, Sagawa H, Yoshida S 2012 Phys. Rev. Lett. 108 052501.
[10] Geng L, Toki H, Meng J 2005 Prog. Theor. Phys 113 785.
[11] Hua X M, Heng T H, Niu Z M, Sun B H, Guo J Y 2012 Sci. China Phys. Mech. Astron. 55 2414.
[12] Möller P, Sierk A J, Ichikawa T, Sagawa H 2016 At. Data Nucl. Data Tables 109-110 1.
[13] Möller P, Mumpower M R, Kawano T, Myers W D 2019 At. Data Nucl. Data Tables 125 1.
[14] Wang N, Liu M, Wu X Z, Meng J 2014 Phys. Lett. B 734 215.
[15] Neufcourt L, Cao Y C, Nazarewicz W, Viens F 2018 Phys. Rev. C 98 034318.
[16] Otsuka T, Suzuki T, Fujimoto R, Grawe H, Akaishi Y 2005 Phys. Rev. Lett. 95 232502.
[17] Sorlin O, Porquet M G 2008 Prog. Part. Nucl. Phys. 61 602.
[18] Utama R, Piekarewicz J, Prosper H B 2016 Phys. Rev. C 93 014311.
[19] Gao Z P, Wang Y J, Lü H L, Li Q F, Shen C W, Liu L 2021 Nucl. Sci. Tech. 32 109.
[20] Ming X C, Zhang H F, Xu R R, Sun X D, Tian Y, Ge Z G 2022 Nucl. Sci. Tech. 33 48.
[21] Wu X H, Lu Y Y, Zhao P W 2022 Phys. Lett. B 834 137394.
[22] Mumpower M R, Sprouse T M, Lovell A E, Mohan A T 2022 Phys. Rev. C 106 L021301.
[23] Niu Z M, Liang H Z 2018 Phys. Lett. B 778 48.
[24] Wu X H, Guo L H, Zhao P W 2021 Phys. Lett. B 819 136387.
[25] Niu Z M, Liang H Z 2022 Phys. Rev. C 106 L021303.
[26] Lu Y H, Shang T S, Du P X, Li J, Liang H Z, Niu Z M 2025 Phys. Rev. C 111 014325.
[27] Chen C Y, Chen A X, Qi X Q, Wang H K 2025 Acta Phys. Sin. 74 012101. (in Chinese) [陈存宇,陈爱喜, 戚晓秋, 王韩奎 2025 物理学报 74 012101].
[28] Tao S J, Zhang L F, Zhang Q Y, Liu J, Xu C 2022 Sci. China Phys. Mech. Astron. 52 252009 (in Chinese) [陶世杰, 张力菲, 张庆一, 刘健, 许昌 2022 中国科学：物理学力学天文学 52 252009].
[29] Li Z L, Wang Y J, Li Q F, Lv B F 2025 Phys. Rev. C 112 014312.
[30] Costiris N J, Mavrommatis E, Gernoth K A, Clark J W 2009 Phys. Rev. C 80 044332.
[31] Niu Z M, Liang H Z, Sun B H, Long W H, Niu Y F 2019 Phys. Rev. C 99 064307.
[32] Li P, Bai J H, Niu Z M, Niu Y F 2022 Sci. China Phys. Mech. Astron. 52 252006. (in Chinese) [李鹏, 白景虎, 牛中明, 牛一斐 2022 中国科学：物理学力学天文学 52 252006]
[33] Wei K W, Shang T S, Tian R H, Yang D, Li C J, Chen J, Li J, Huang X L, Zhu J L 2025 Acta Phys. Sin. 182901. (in Chinese) [魏凯文, 尚天帅, 田榕赫, 杨东, 李春娟, 陈军, 李剑, 黄小龙, 朱佳丽 2025 物理学报 74 182901]
[34] Li W F, Zhang X Y, Niu Y F, Niu Z M 2024 J. Phys. G: Nucl. Part. Phys. 51 015103.
[35] Li P, Niu Y F, Niu Z M 2025 Nucl. Sci. Tech. 36 50.
[36] Jin Z S, Yan M S, Zhou H, Cheng A, Ren Z Z, Liu J 2023 Phys. Rev. C 108 014326.
[37] Liu J, Jin Z S, Ren Z Z 2025 Phys. Rev. C 112 024309.
[38] Ma N N, Zhao T L, Wang W X, Zhang H F 2023 Phys. Rev. C 107 014310.
[39] Chen H J, Sheng H W, Huang W H, Wu L Q, Zhao T L, Bao X J 2025 Acta Phys. Sin. 74 192301. (in Chinese) [陈海军, 盛浩文, 黄文豪, 吴彬琪, 赵天亮, 包小军 2025 物理学报 74 192301].
[40] Wang Z A, Pei J C, Liu Y, Qiang Y 2019 Phys. Rev. Lett. 123 122501.
[41] Qiao C Y, Pei J C, Wang Z A, Qiang Y, Chen Y J 2021 Phys. Rev. C 103 034621.
[42] Zhao Q, Ren Z X, Zhao P W, Meng J 2022 Phys. Rev. C 106 034315.
[43] Mumpower M R, Surman R, McLaughlin G C, Aprahamian A 2016 Prog. Part. Nucl. Phys. 86 86.
Metrics
- Abstract views: 11
- PDF Downloads: 0
- Cited By: 0
下载:
