-
This study applies machine learning, specifically transfer learning with neural networks, to improve predictions of fission barrier heights and ground state binding energies of superheavy nuclei, which are crucial for calculating survival probabilities in fusion reactions. Transfer learning for neural networks proceeds in two stages: pre-training and fine-tuning, each driven by a distinct pre-training data set and target data set. In this work we split the pre-training data into 60 % for training and 40 % for validation, while the target data are partitioned into 20 % test, with the remaining 80 % further divided into 60 % training and 40 % validation. To construct the neural-network model we adopt the proton number Z and mass number A as the input layer, employ two hidden layers each containing 128 neurons with ReLU (Rectified Linear Unit) activation, and set the learning rate to 0.001. For the fission-barrier-height model, the pre-training dataset is either the FRLDM or the WS4 model data, and the experimental measurements serve as the target set. For the ground-state binding-energy model, we first form the residuals between WS4 predictions and the AME2020 evaluation, then separate these residuals into a light-nucleus subset and a heavy-nucleus subset according to proton number. The light-nucleus subset is used for pre-training and the heavy-nucleus subset for fine-tuning. After optimization, the root-mean-square error (RMSE) of the FRLDM barrier model falls from 1.03 MeV to 0.60 MeV, and that of the WS4 barrier model drops from 0.97 MeV to 0.61 MeV. For the binding-energy model, the RMSE decreases from 0.33 MeV to 0.17 MeV on the test set and from 0.29 MeV to 0.26 MeV on the full data set. We also present the performance of the fission-barrier model before and after refinement, together with the predicted barrier heights along the isotopic chains of the new elements Z = 119 and Z = 120, and analyzed the reasons for the differences in the results obtained by different models. We hope that these results are intended to provide a useful reference for future theoretical studies. The datasets in this paper are openly available at https://www.doi.org/10.57760/sciencedb.28388(Please use private access link https://www.scidb.cn/s/6fmeIz to access the dataset during the peer review process).
-
Keywords:
- Fission barriers /
- Binding energies /
- Machine learning /
- Superheavy nuclei
-
[1] Schmidt K H, Jurado B 2018 Rep. Prog. Phys. 81 106301 [2] Audi G, Bersillon O, Blachot J, Wapstra A H 2003 Nucl. Phys. A 729 3 [3] Audi G, Wapstra A H, Thibault C 2003 Nucl. Phys. A 729 337 [4] Wang N, Liu M 2024 Chin. Phys. C 48 094103 [5] Bayram T, Akkoyun S, Kara S O 2014 Ann. Nucl. Energy 63 172 [6] Oganessian Y T, Utyonkov V K, Lobanov Y V, Abdullin F S, Polyakov A N, Sagaidak R N, Shirokovsky I V, Tsyganov Y S, Voinov A A, Gulbekian G G, Bogomolov S L, Gikal B N, Mezentsev A N, Iliev S, Subbotin V G,Sukhov A M,Subotic K, Zagrebaev V I, Vostokin G K, Itkis M G, Moody K J, Patin J B, Shaughnessy D A, Stoyer M A, Stoyer N J, Wilk P A, Kenneally J M, Landrum J H, Wild J F, Lougheed R W 2006 Phys. Rev. C 74 044602 [7] Oganessian Y T, Utyonkov V K, Lobanov Y V, Abdullin F S, Polyakov A N, Shirokovsky I V, Tsyganov Y S, Gulbekian G G, Bogomolov S L, Gikal B N, Mezenstev A N, Iliev S, Subbotin V G, Sukhov A M, Voinov A A, Buklanov G V, Subotic K, Zagrebaev V I, Itkis M G, Patin J B, Moody K J, Wild J F, Stoyer M A, Stoyer N J, Shaughnessy D A, Kenneally J M, Wilk P A, Lougheed R W, Il’kaev R I, Vesnovskii S P 2004 Phys. Rev. C 70 064609 [8] Rana S, Kumar R, Bhuyan M 2021 Phys. Rev. C 104 024619 [9] Jadambaa K 2017 EPJ Web Conf. 163 00030 [10] Schädel M 2015 Philos. Trans. R. Soc. A 373 2037. [11] Li J X, Zhang H F 2022 Phys. Rev. C 105 054606 [12] Feng Z Q, Jin G M, Fu F, Li J Q 2006 Nucl. Phys. A 771 50 [13] Zhu L, Xie W J, Zhang F S 2014 Phys. Rev. C 89 024615 [14] Qiang Y, Deng X Q, Shi Y, Qiao C Y, Pei J C 2024 Phys. Lett. B 858 139057 [15] Hofmann S, Münzenberg G 2000 Rev. Mod. Phys. 72 733 [16] Lunney D, Pearson J M, Thibault C 2003 Rev. Mod. Phys. 75 1021 [17] Ma N N, Zhang H F, Bao X J, Zhang H F 2019 Chin. Phys. C 43 044105 [18] Zhang H F, Wang L H, Yin J P, Chen P H, Zhang H F 2017 J. Phys. G: Nucl. Part. Phys. 44 045110 [19] Naqa I E, Murphy M J 2015 What Is Machine Learning? (Germany: Springer Nature) pp21-37 [20] Goh A T C 1995 Artif. Intell. Eng. 9 143 [21] Bayram T, Akkoyun S, Kara S O 2014 J. Phys.: Conf. Ser. 490 012105 [22] Akkoyun S, Bayram T, Turker T 2014 Radiat. Phys. Chem. 96 186 [23] Athanassopoulos S, Mavrommatis E, Gernoth K A, Clark J W 2004 Nucl. Phys. A 743 222 [24] Wen H F, Shang T S, Li J, Niu Z M, Yang D, Xue Y H, Li X, Huang X L 2023 Acta Phys. Sin. 72 152101 (in Chinese) [温湖峰, 尚天帅, 李剑, 牛中明, 杨东, 薛永和, 李想, 黄小龙 2023 72 152101] [25] Zhang W J, Zhang Z Y, Hu J F, Lu B N, Pang J Y, Wang Q 2025 Chin. Phys. Lett. 42 070202 [26] Li J X, Zhang H F 2024 Phys. Rev. C 110 034608 [27] Graczyk K M, Kowal B E, Ankowski A M, Banerjee R D, Bonilla J L, Prasad H, Sobczyk J T 2025 Phys. Rev. Lett. 135 052502 [28] Yuan L, Li J X, Zhang H F 2024 Chin. Phys. C 48 064106 [29] Möller P, Sierk A J, Ichikawa T, Iwamoto A, Bengtsson R, Uhrenholt H, Åberg S 2009 Phys. Rev. C 79 064304 [30] Wang N, Liu M 2024 Chin. Phys. C 48 094103 [31] Wang M, Huang W J, Kondev F G, Audi G, Naimi S 2021 Chin. Phys. C 45 030003 [32] Wang N, Liu M, Wu X Z, Meng J 2024 Phys. Lett. B 734 215 [33] Capote R, Herman M, Obložinský P, Young P G, Goriely S, Belgya T, Ignatyuk A V, Koning A J, Hilaire S, Plujko V A, Avrigeanu M, Bersillon O, Chadwick M B, Fukahori T, Ge Zhigang, Han Yinlu, Kailas S, Kopecky J, Maslov V M, Reffo G, Sin M, Soukhovitskii E Sh, Talou P 2009 Nucl. Data Sheets 110 3107
Metrics
- Abstract views: 14
- PDF Downloads: 0
- Cited By: 0
下载:
