Citing Articles For: Physically interpretable machine learning for nuclear masses

Nuclear mass predictions based on a convolutional neural network

Yanhua Lu, Tianshuai Shang, Pengxiang Du, Jian Li, Haozhao Liang, and Zhongming Niu

Phys. Rev. C 111 014325 (2025)

Model-repair capabilities of tree-based machine-learning algorithms applied to theoretical nuclear mass models

Guo-Ping Liu, Hua-Lei Wang, Zhen-Zhen Zhang, and Min-Liang Liu

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Kolmogorov-Arnold networks in nuclear binding energy prediction

Hao Liu, Jin Lei, and Zhongzhou Ren

Phys. Rev. C 111 024316 (2025)

Nuclear β -decay half-life predictions and r -process nucleosynthesis using machine learning models

Amir Jalili, Feng Pan, Y. A. Luo, and Jerry P. Draayer

Phys. Rev. C 111 034321 (2025)

Predictions from several models for the cross sections of light neutron-rich isotopes by Qg systematics in Ar40 projectile-fragmentation reactions

Xiao-Bao Wei, Hui-Ling Wei, Chun-Wang Ma, Chun-Yuan Qiao, Ya-Fei Guo, Jie Pu, Kai-Xuan Cheng, Yu-Ting Wang, Zeng-Xiang Wang, Tian-Ren Zhuo, Dan Peng, Shi-Tao Wang, Shu-Wen Tang, Yu-Hong Yu, Xue-Heng Zhang, Ya-Zhou Sun, Shu-Ya Jin, Guo-Li Zhang, Xuan Jiang, Zhi-Yao Li, Ying-Feng Xu, Fen-Hua Lu, and Tuo-Qi Liu

Phys. Rev. C 111 034607 (2025)

Understanding on prediction differences among theoretical mass models with machine learning techniques

Weihu Ye and Niu Wan

Phys. Rev. C 111 044317 (2025)

Probing the refined performance of the categorical-boosting algorithm to the Hartree-Fock-Bogoliubov mass model with several Skyrme forces

Jin-Liang Guo, Hua-Lei Wang, Zhen-Zhen Zhang, and Min-Liang Liu

Phys. Rev. C 111 054322 (2025)

Machine learning for decoding spin-zero and octupole excitations

B. F. Lv, Yongjia Wang, Zhilong Li, and C. M. Petrache

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Prediction of the first $$2^{+}$$ states properties for atomic nuclei using light gradient boosting machine

Hui Liu, Xin-Xiang Li, Yun Yuan, Wen Luo, and Yi Xu

NUCL SCI TECH 36 21 (2025)

Systematic evaluation of half-lives of proton emission, α decay, cluster radioactivity and spontaneous fission based on a Bayesian neural network

Hai Fei Zhang

J. Phys. G: Nucl. Part. Phys. 52 095103 (2025)

A Novel Bayesian Neural Network Approach for Nuclear Root-Mean-Square Charge Radii

X. Zhang, X. Liu, H. Zheng, W. Lin, R. Wada, J. Han, C. W. Ma, C. Y. Qiao, D. Peng, Y. Huang, Q. Leng, G. Qu, P. Ren, and Z. Yang

IEEE Trans. Nucl. Sci. 72 795 (2025)

Implication of odd–even staggering in the charge radii of calcium isotopes

Rong An, Xiang Jiang, Na Tang, Li-Gang Cao, and Feng-Shou Zhang

Commun. Theor. Phys. 77 105301 (2025)

Discovering nuclear models from symbolic machine learning

Jose M. Munoz, Silviu M. Udrescu, and Ronald F. Garcia Ruiz

Commun Phys 8 101 (2025)

Predicting Nuclear Level Density Using a Physics-Informed Neural Network with Multi-Task Learning

Bora Canbula

Applied Sciences 16 312 (2025)

$$\alpha$$-decay half-life predictions with support vector machine

Amir Jalili, Feng Pan, Jerry P. Draayer, Ai-Xi Chen, and Zhongzhou Ren

Sci Rep 14 30776 (2024)

Mapping low-lying states and B(E2;01+→21+) in even-even nuclei with machine learning

B.F. Lv, Z.L. Li, Y.J. Wang, and C.M. Petrache

Physics Letters B 857 139013 (2024)

Atomic masses with machine learning for the astrophysical r process

Mengke Li, Trevor M. Sprouse, Bradley S. Meyer, and Matthew R. Mumpower

Physics Letters B 848 138385 (2024)

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

W. S. Porter, B. Liu, D. Ray, A. A. Valverde, M. Li, M. R. Mumpower, M. Brodeur, D. P. Burdette, N. Callahan, A. Cannon, J. A. Clark, D. E. M. Hoff, A. M. Houff, F. G. Kondev, A. E. Lovell, A. T. Mohan, G. E. Morgan, C. Quick, G. Savard, K. S. Sharma, T. M. Sprouse, and L. Varriano

Phys. Rev. C 110 034321 (2024)

Research on charged particle identification of telescope in heavy-ion collisions at low and intermediate energies based on optimization algorithms

GaoYi Cheng, XiGuang Cao, QianMin Su, Liu Yang, and GuoQiang Zhang

Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 554 165453 (2024)

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Articles Citing

Physically interpretable machine learning for nuclear masses

Nuclear mass predictions based on a convolutional neural network

Model-repair capabilities of tree-based machine-learning algorithms applied to theoretical nuclear mass models

Kolmogorov-Arnold networks in nuclear binding energy prediction

Nuclear β -decay half-life predictions and r -process nucleosynthesis using machine learning models

Predictions from several models for the cross sections of light neutron-rich isotopes by Qg systematics in Ar40 projectile-fragmentation reactions

Understanding on prediction differences among theoretical mass models with machine learning techniques

Probing the refined performance of the categorical-boosting algorithm to the Hartree-Fock-Bogoliubov mass model with several Skyrme forces

Machine learning for decoding spin-zero and octupole excitations

Prediction of the first $$2^{+}$$ states properties for atomic nuclei using light gradient boosting machine

Systematic evaluation of half-lives of proton emission, α decay, cluster radioactivity and spontaneous fission based on a Bayesian neural network

A Novel Bayesian Neural Network Approach for Nuclear Root-Mean-Square Charge Radii

Implication of odd–even staggering in the charge radii of calcium isotopes

Discovering nuclear models from symbolic machine learning

Predicting Nuclear Level Density Using a Physics-Informed Neural Network with Multi-Task Learning

$$\alpha$$-decay half-life predictions with support vector machine

Mapping low-lying states and B(E2;01+→21+) in even-even nuclei with machine learning

Atomic masses with machine learning for the astrophysical r process

Investigating the effects of precise mass measurements of Ru and Pd isotopes on machine learning mass modeling

Nonempirical shape dynamics of heavy nuclei with multitask deep learning

Importance of physical information on the prediction of heavy-ion fusion cross sections with machine learning

Nuclear binding energies in artificial neural networks

Uncertainty quantification of mass models using ensemble Bayesian model averaging

Novel Bayesian probability method in predictions of nuclear masses

Nuclear mass predictions using machine learning models

Research on charged particle identification of telescope in heavy-ion collisions at low and intermediate energies based on optimization algorithms

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