Citing Articles For: Physically interpretable machine learning for nuclear masses

Implications of a weakening N=126 shell closure away from stability for r-process astrophysical conditions

Mengke Li, Gail C. McLaughlin, and Rebecca Surman

Physics Letters B 872 140109 (2026)

Machine learning-driven high-precision model for α-decay energy and half-life prediction of superheavy nuclei

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Phys. Scr. 101 176004 (2026)

From α decay to cluster decay: An extreme case of transfer learning

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Phys. Rev. C 114 014314 (2026)

Bidirectional neural networks for global nucleon-nucleus optical model calculations

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Machine-learning the impact parameter in heavy-ion collisions at sNN=4 and 11 GeV: A cross-check study with UrQMD, AMPT, and JAM

Xiaoqing Yue, Guojun Wei, Yongjia Wang, Zhilong Li, Pengcheng Li, Haojie Xu, Xiangrong Zhu, Qingfeng Li, Fuhu Liu, and Yasushi Nara

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Correcting nuclear mass models with interpretable machine learning to bridge theory and data

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Phys. Rev. C 114 034310 (2026)

Physics-structured cooperative neural network for baseline-free nuclear mass modeling

Peiwen Zai, Wei Cheng, and Feng-Shou Zhang

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α -decay half-lives of superheavy nuclei with support-vector regression

Haitao Yang, Xiaopan Li, Xiefei Song, Dianxu Ma, Gongming Yu, and Xiaojun Bao

Phys. Rev. C 113 014307 (2026)

Simultaneous improvements of nuclear mass and charge radius predictions using multitask Gaussian process approaches

Weihu Ye and Niu Wan

Phys. Rev. C 113 024304 (2026)

Robust extrapolation in nuclear mass predictions using domain-informed activation functions

C. H. Kim, K. Y. Chae, and M. S. Smith

Phys. Rev. C 113 024308 (2026)

Error analysis of the finite-range liquid-drop model

C. Azam, D. Blondeau-Patissier, P. Tamagno, M. Frosini, and A. Pastore

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Deep learning for nuclear masses in deformed relativistic Hartree-Bogoliubov theory in continuum

Soonchul Choi, Kyungil Kim, Zhenyu He, Yongbeom Choi, Youngman Kim, and Toshitaka Kajino

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Deep learning approach for predicting multiple observables in Au+Au collisions at energies available at the BNL Relativistic Heavy Ion Collider

Jun-Qi Tao, Xiang Fan, Yang Liu, Yu Sha, Kai Zhou, Hua Zheng, and Ben-Wei Zhang

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Studying the incomplete fusion with physics-constrained machine learning approaches

I.M. Bhat, B.F. Lv, C.M. Petrache, and Y.J. Wang

Nuclear Physics A 1074 123457 (2026)

High-precision ab initio nuclear theory: learning to overcome model-space limitations

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J. Phys. G: Nucl. Part. Phys. 53 083001 (2026)

Prediction of $$2^{+}$$ energies in even–even nuclei of the Terra incognita with Bayesian neural network

Xin Zhang and Lorenzo Fortunato

Eur. Phys. J. Plus 141 121 (2026)

Meson Properties and Symmetry Emergence Based on the Deep Neural Network

Xin Tong, Wei Feng, Weiwei Xu, Chao-Hsi Chang, Guo-Li Wang, and Qiang Li

Chinese Phys. Lett. 43 020201 (2026)

Study of yrast and yrare low-lying excited states using machine learning approaches*

Zhi Long 志龙 Li 李, Bing Feng 冰锋 Lv 吕, Yong Jia 永佳 Wang 王, and C. M. Petrache

Chinese Phys. C 50 014107 (2026)

Predictions of neutron-capture reaction cross sections using a Bayesian neural network approach combined with a physically motivated empirical formula*

H. B. 鸿博 Li 李, W. F. 伟峰 Li 李, Q. 庆 Wu 武, and Z. M. 中明 Niu 牛

Chinese Phys. C 50 064102 (2026)

Prediction of atomic nuclear mass using neural networks constrained by neutron and proton separation energy

Dongdong WANG, Peng Li, and Zhiheng WANG

Acta Phys. Sin. 75 020104 (2026)

No title available

Ziyuan Li, Paulo S. A. Freitas, John W. Clark, and Babette Dellen

Bayesian model mixing with multireference energy density functional

Aman Sharma, Nicolas Schunck, and Kyle Wendt

Phys. Rev. Research 7 023179 (2025)

Machine-learning predictions for the nuclear charge radius: Bayesian method versus decision-tree-based algorithm

Zhilong Li, Yongjia Wang, Qingfeng Li, and Bing-feng Lv

Phys. Rev. C 112 014312 (2025)

Deep learning approaches for nuclear binding energy predictions: A comparative study of RNN, GRU, and LSTM neural-network models

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

Phys. Rev. C 112 024305 (2025)

Quantifying uncertainty in machine learning for nuclear binding energy

Mengyao Huang, Kyle A. Wendt, Nicolas F. Schunck, and Erika M. Holmbeck

Phys. Rev. C 112 034317 (2025)

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Articles Citing

Physically interpretable machine learning for nuclear masses

Implications of a weakening N=126 shell closure away from stability for r-process astrophysical conditions

Machine learning-driven high-precision model for α-decay energy and half-life prediction of superheavy nuclei

From α decay to cluster decay: An extreme case of transfer learning

Bidirectional neural networks for global nucleon-nucleus optical model calculations

Machine-learning the impact parameter in heavy-ion collisions at sNN=4 and 11 GeV: A cross-check study with UrQMD, AMPT, and JAM

Correcting nuclear mass models with interpretable machine learning to bridge theory and data

Physics-structured cooperative neural network for baseline-free nuclear mass modeling

α -decay half-lives of superheavy nuclei with support-vector regression

Simultaneous improvements of nuclear mass and charge radius predictions using multitask Gaussian process approaches

Robust extrapolation in nuclear mass predictions using domain-informed activation functions

Error analysis of the finite-range liquid-drop model

Deep learning for nuclear masses in deformed relativistic Hartree-Bogoliubov theory in continuum

Deep learning approach for predicting multiple observables in Au+Au collisions at energies available at the BNL Relativistic Heavy Ion Collider

Studying the incomplete fusion with physics-constrained machine learning approaches

High-precision ab initio nuclear theory: learning to overcome model-space limitations

Prediction of $$2^{+}$$ energies in even–even nuclei of the Terra incognita with Bayesian neural network

Meson Properties and Symmetry Emergence Based on the Deep Neural Network

Study of yrast and yrare low-lying excited states using machine learning approaches*

Predictions of neutron-capture reaction cross sections using a Bayesian neural network approach combined with a physically motivated empirical formula*

Prediction of atomic nuclear mass using neural networks constrained by neutron and proton separation energy

No title available

Bayesian model mixing with multireference energy density functional

Machine-learning predictions for the nuclear charge radius: Bayesian method versus decision-tree-based algorithm

Deep learning approaches for nuclear binding energy predictions: A comparative study of RNN, GRU, and LSTM neural-network models

Quantifying uncertainty in machine learning for nuclear binding energy

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