Abstract
Background:
The masses of \(\sim\)2500 nuclei have been measured experimentally; however, >7000 isotopes are predicted to exist in the nuclear landscape from H (\(Z=1\)) to Og (\(Z=118\)) based on various theoretical calculations. Exploring the mass of the remaining isotopes is a popular topic in nuclear physics. Machine learning has served as a powerful tool for learning complex representations of big data in many fields.
Purpose:
We use Light Gradient Boosting Machine (LightGBM), which is a highly efficient machine learning algorithm, to predict the masses of unknown nuclei and to explore the nuclear landscape on the neutron-rich side from learning the measured nuclear masses.
Methods:
Several characteristic quantities (e.g., mass number and proton number) are fed into the LightGBM algorithm to mimic the patterns of the residual \(\delta (Z,A)\) between the experimental binding energy and the theoretical one given by the liquid-drop model (LDM), Duflo–Zucker (DZ, also dubbed DZ28) mass model, finite-range droplet model (FRDM, also dubbed FRDM2012), as well as the Weizsäcker–Skyrme (WS4) model to refine these mass models.
Results:
By using the experimental data of 80\(\%\) of known nuclei as the training dataset, the root mean square deviations (RMSDs) between the predicted and the experimental binding energy of the remaining 20% are approximately \(0.234\pm 0.022\), \(0.213\pm 0.018\), \(0.170\pm 0.011\), and \(0.222\pm 0.016\) MeV for the LightGBM-refined LDM, DZ model, WS4 model, and FRDM, respectively. These values are approximately 90%, 65%, 40%, and 60% smaller than those of the corresponding origin mass models. The RMSD for 66 newly measured nuclei that appeared in AME2020 was also significantly improved. The one-neutron and two-neutron separation energies predicted by these refined models are consistent with several theoretical predictions based on various physical models. In addition, the two-neutron separation energies of several newly measured nuclei (e.g., some isotopes of Ca, Ti, Pm, and Sm) predicted with LightGBM-refined mass models are also in good agreement with the latest experimental data.
Conclusions:
LightGBM can be used to refine theoretical nuclear mass models and predict the binding energy of unknown nuclei. Moreover, the correlation between the input characteristic quantities and the output can be interpreted by SHapley additive exPlanations (a popular explainable artificial intelligence tool), which may provide new insights for developing theoretical nuclear mass models.
Similar content being viewed by others
Change history
05 November 2021
A Correction to this paper has been published: https://doi.org/10.1007/s41365-021-00958-z
References
D. Lunney, J.M. Pearson, C. Thibault, Recent trends in the determination of nuclear masses. Rev. Mod. Phys. 75, 1021–1082 (2003). https://doi.org/10.1103/RevModPhys.75.1021
K. Blaum, High-accuracy mass spectrometry with stored ions. Phys. Rep. 425, 1–78 (2006). https://doi.org/10.1016/j.physrep.2005.10.011
F. Wienholtz, D. Beck, K. Blaum et al., Masses of exotic calcium isotopes pin down nuclear forces. Nature 498, 346–349 (2013). https://doi.org/10.1038/nature12431
K. Blaum, J. Dilling, W. Nörtershäuser, Precision atomic physics techniques for nuclear physics with radioactive beams. Phys. Scr. 2013, 014017 (2013). https://doi.org/10.1088/0031-8949/2013/T152/014017
Z. Niu, H. Liang, B. Sun et al., High precision nuclear mass predictions towards a hundred kilo-electron-volt accuracy. Sci. Bull. 63, 759–764 (2018). https://doi.org/10.1016/j.scib.2018.05.009
M. Wang, Y.H. Zhang, X.H. Zhou, Nuclear mass measurements. Sci. China Phys. Mech. Astron. 50, 052006 (2020). https://doi.org/10.1360/SSPMA-2019-0308
C. Ma, M. Bao, Z.M. Niu et al., New extrapolation method for predicting nuclear masses. Phys. Rev. C 101, 045204 (2020). https://doi.org/10.1103/PhysRevC.101.045204
M. Wang, W.J. Huang, F.G. Kondev et al., AME 2020 atomic mass evaluation (II) Tables, graphs, and references. Chin. Phys. C 45, 030003 (2021). https://doi.org/10.1088/1674-1137/abddaf
P. Möller, W.D. Myers, H. Sagawa et al., New finite-range droplet mass model and equation-of-state parameters. Phys. Rev. Lett. 108, 052501 (2012). https://doi.org/10.1103/PhysRevLett.108.052501
P. Möller, J.R. Nix, W.D. Myers et al., Nuclear ground state masses and deformations. Atomic Data Nucl. Data Tabels. 59, 185–381 (1995). https://doi.org/10.1006/adnd.1995.1002
N. Wang, M. Liu, X. Wu et al., Surface diffuseness correction in global mass formula. Phys. Lett. B 734, 215–219 (2014). https://doi.org/10.1016/j.physletb.2014.05.049
S. Goriely, N. Chamel, J.M. Pearson, Further explorations of the Skyrme-Hartree-Fock-Bogoliubov mass formulas. XVI. Inclusion of self-energy effects in pairing. Phys. Rev. C 93, 034337 (2016). https://doi.org/10.1103/PhysRevC.93.034337
S. Goriely, N. Chamel, J.M. Pearson, Skyrme-Hartree-Fock-Bogoliubov nuclear mass formulas: Crossing the 0.6 MeV threshold with microscopically deduced pairing. Phys. Rev. Lett. 102, 152503 (2009). https://doi.org/10.1103/PhysRevLett.102.152503
Y. Aboussir, J.M. Pearson, A.K. Dutta et al., Nuclear mass formula via an approximation to the Hartree-Fock method. Atom. Data Nucl. Data Tabl. 61, 127–176 (1995). https://doi.org/10.1016/S0092-640X(95)90014-4
L.S. Geng, H. Toki, J. Meng, Masses, deformations, and charge radii: Nuclear ground-state properties in relativistic mean field model. Prog. Theor. Phys. 113, 785–800 (2005). https://doi.org/10.1143/PTP.113.785
X.W. Xia, Y. Lim, P.W. Zhao et al., The limits of the nuclear landscape explored by the relativistic continuum Hartree-Bogoliubov theory. Atomic Data Nucl. Data Tabels. 121–122, 1–215 (2018). https://doi.org/10.1016/j.adt.2017.09.001
C. Giuseppe, C. Ignacio, C. Kyle et al., Machine learning and the physical sciences. Rev. Mod. Phys. 91, 045002 (2019). https://doi.org/10.1103/RevModPhys.91.045002
A. Radovic, M. Williams, D. Rousseau et al., Machine learning at the energy and intensity frontiers of particle physics. Nature 560, 41 (2018). https://doi.org/10.1038/s41586-018-0361-2
J. Xu, Constraining isovector nuclear interactions with giant dipole resonance and neutron skin in \(^{208}\) Pb from a bayesian approach. Chin. Phys. Lett. 38, 042101 (2021). https://doi.org/10.1088/0256-307X/38/4/042101
H.B. Ren, L. Wang, X. Dai, Machine learning kinetic energy functional for a one-dimensional periodic system. Chin. Phys. Lett. 38, 050701 (2021). https://doi.org/10.1088/0256-307X/38/5/050701
X.H. Wu, L.H. Guo P.W. Zhao, Nuclear masses in extended kernel ridge regression with odd-even effects. Phys. Lett. B 819, 136387 (2021). https://doi.org/10.1016/j.physletb.2021.136387
S.J. Lei, D. B, Z.Z. Ren et al., Finding short-range parity-time phase-transition points with a neural network. Chin. Phys. Lett. 38, 051101 (2021). https://doi.org/10.1088/0256-307X/38/5/051101
W.J. Rao, Machine learning for many-body localization transition. Chin. Phys. Lett. 37, 080501 (2020). https://doi.org/10.1088/0256-307X/37/8/080501
H.L. Liu, D.D. Han, P. Ji et al, Reaction rate weighted multilayer nuclear reaction network. Chin. Phys. Lett. 37, 112601 (2020). https://doi.org/10.1088/0256-307X/37/11/112601
R. Utama, J. Piekarewicz, H.B. Prosper, Nuclear mass predictions for the crustal composition of neutron stars: A bayesian neural network approach. Phys. Rev. C 93, 014311 (2016). https://doi.org/10.1103/PhysRevC.93.014311
R. Utama, J. Piekarewicz, Refining mass formulas for astrophysical applications: a Bayesian neural network approach. Phys. Rev. C 96, 044308 (2017). https://doi.org/10.1103/PhysRevC.96.044308
Z.M. Niu, H.Z. Liang, Nuclear mass predictions based on Bayesian neural network approach with pairing and shell effects. Phys. Lett. B 778, 48–53 (2018). https://doi.org/10.1016/j.physletb.2018.01.002
R. Utama, W.C. Chen J. Piekarewicz, Nuclear charge radii: Density functional theory meets Bayesian neural networks. J. Phys. G 43, 114002 (2016). https://doi.org/10.1088/0256-307X/38/5/051101
Z.A. Wang, J.C. Pei, Y. Liu, Bayesian evaluation of incomplete fission yields. Phys. Rev. Lett 123, 122501 (2019). https://doi.org/10.1103/PhysRevLett.123.122501
C.W. Ma, D. Peng, H.L. Wei et al., A Bayesian-Neural-Network prediction for fragment production in proton induced spallation reaction. Chin. Phys. C 44, 124107 (2020). https://doi.org/10.1088/1674-1137/abb657
C.W. Ma, D. Peng, H.L. Wei et al., Isotopic cross-sections in proton-induced spallation reactions based on Bayesian neural network method. Chin. Phys. C 44, 014104 (2020). https://doi.org/10.1088/1674-1137/44/1/014104
Z.M. Niu, H.Z. Liang, B.H. Sun et al., Predictions of nuclear \(\beta\) -decay half-lives with machine learning and their impact on r-process nucleosynthesis. Phys. Rev. C 99, 064307 (2019). https://doi.org/10.1103/PhysRevC.99.064307
L.G. Pang, K. Zhou, N. Su et al., An equation-of-state meter of quantum chromodynamics transitions from deep learning. Nat. Commun. 9, 210 (2018). https://doi.org/10.1038/s41467-017-02726-3
Y.L. Du, K. Zhou, J. Steinheimer et al., Identifying the nature of the QCD transition in relativistic collisions of heavy nuclei with deep learning. Eur. Phys. J. C 80, 516 (2020). https://doi.org/10.1140/epjc/s10052-020-8030-7
J. Steinheimer, L. Pang, K. Zhou et al., A machine learning study to identify spinodal clumping in high energy nuclear collisions. JHEP 12, 122 (2019). https://doi.org/10.1007/JHEP12(2019)122
Y.D. Song, R. Wang, Y.G. Ma et al., Determining the temperature in heavy-ion collisions with multiplicity distribution. Phys. Lett. B 814, 136084 (2021). https://doi.org/10.1016/j.physletb.2021.136084
R. Wang, Y.G. Ma, R. Wada et al., Nuclear liquid-gas phase transition with machine learning. Phys. Rev. Res. 2, 043202 (2020). https://doi.org/10.1103/PhysRevResearch.2.043202
F.P. Li, Y.J. Wang, H.L. Lü et al., Application of artificial intelligence in the determination of impact parameters in heavy-ion collisions at intermediate energies. J. Phys. G 47, 115104 (2020). https://doi.org/10.1088/1361-6471/abb1f9
H.F. Zhang, L.H. Wang, J.P. Yin et al., Performance of the Levenberg–Marquardt neural network approach in nuclear mass prediction. J. Phys. G 44, 045110 (2017). https://doi.org/10.1088/1361-6471/aa5d78
X.H. Wu, P.W. Zhao, Predicting nuclear masses with the kernel ridge regression. Phys. Rev. C 101, 051301 (2020). https://doi.org/10.1103/PhysRevC.101.051301
Z.M. Niu, J.Y. Fang, Y.F. Niu, Comparative study of radial basis function and Bayesian neural network approaches in nuclear mass predictions. Phys. Rev. C 100, 054311 (2019). https://doi.org/10.1103/PhysRevC.100.054311
M. Shelley, A. Pastore, A new mass model for nuclear astrophysics: crossing 200 keV accuracy. arXiv:2102.07497 (2021)
L. Neufcourt, Y. Cao, W. Nazarewicz et al., Bayesian approach to model-based extrapolation of nuclear observables. Phys. Rev. C 98, 034318 (2018). https://doi.org/10.1103/PhysRevC.98.034318
M. Carnini, A. Pastore, Trees and forests in nuclear physics. J. Phys. G 47, 082001 (2020). https://doi.org/10.1088/1361-6471/ab92e3
E. Yüksel, D. Soydaner, H. Bahtiyar, Nuclear mass predictions using neural networks: application of the multilayer perceptron. arXiv:2101.12117v1 (2021)
K.A. Gernoth, J.W. Clark, J.S. Prater et al., Neural network models of nuclear systematics. Phys. Lett. B 300, 1–7 (1993). https://doi.org/10.1016/0370-2693(93)90738-4
S. Athanassopoulos, E. Mavrommatis, K.A. Gernoth et al., Nuclear mass systematics using neural networks. Nucl. Phys. A 743, 222–235 (2004). https://doi.org/10.1016/j.nuclphysa.2004.08.006
J.W. Clark, H. Li, Application of support vector machines to global prediction of nuclear properties. Int. J. Mod. Phys. B 20, 5015–5029 (2006). https://doi.org/10.1142/S0217979206036053
W. Liu, J.L. Lou, Y.L. Ye et al., Experimental study of intruder components in light neutron-rich nuclei via a single-nucleon transfer reaction. Nucl. Sci. Tech. 31, 20 (2020). https://doi.org/10.1007/s41365-020-0731-y
M. Ji, C. Xu, Quantum anti-zeno effect in nuclear \(\beta\) decay. Chin. Phys. Lett. 38, 032301 (2021). https://doi.org/10.1088/0256-307X/38/3/032301
Y.J. Wang, F.H. Guan, X.Y. Diao et al., CSHINE for studies of HBT correlation in heavy ion reactions. Nucl. Sci. Tech. 32, 4 (2021). https://doi.org/10.1007/s41365-020-00842-2
D.Z. Chen, D.L. Fang, C.L. Bai, Impact of finite-range tensor terms in the Gogny force on the \(\beta\) decay of magic nuclei. Nucl. Sci. Tech. 32, 74 (2021). https://doi.org/10.1007/s41365-021-00908-9
C.J. Jiang, Y. Qiang, D.W. Guan et al., From finite nuclei to neutron stars, the essential role of the high-order density dependence in effective forces. Chin. Phys. Lett. 38, 052101 (2021). https://doi.org/10.1088/0256-307X/38/5/052101
X. Zhou, M. Wang, Y.H. Zhang et al., Charge resolution in the isochronous mass spectrometry and the mass of \(^{51}\)Co. Nucl. Sci. Tech. 32, 37 (2021). https://doi.org/10.1007/s41365-021-00876-0
W. Nan, B. Guo, C.J. Lin et al., First proof-of-principle experiment with the post-accelerated isotope separator on-line beam at BRIF: measurement of the angular distribution of \(^{23}\)Na + \(^{40}\)Ca elastic scattering. Nucl. Sci. Tech. 32, 53 (2021). https://doi.org/10.1007/s41365-021-00889-9
H. Yu, D.Q. Fang, Y.G. Ma, Investigation of the symmetry energy of nuclear matter using isospin-dependent quantum molecular dynamics. Nucl. Sci. Tech. 31, 61 (2020). https://doi.org/10.1007/s41365-020-00766-x
J. Duflo, A.P. Zuker, Microscopic mass formulae. Phys. Rev. C 52, R23 (1995). https://doi.org/10.1103/PhysRevC.52.R23
A. Pastore, D. Neill, H. Powell et al., Impact of statistical uncertainties on the composition of the outer crust of a neutron star. Phys. Rev. C 101, 035804 (2020). https://doi.org/10.1103/PhysRevC.101.035804
N.N. Ma, H.F. Zhang, X.J. Bao et al., Basic characteristics of the nuclear landscape by improved Weizs äcker-Skyrme-type nuclear mass model. Chin. Phys. C 43, 044105 (2019). https://doi.org/10.1088/1674-1137/43/4/044105
H.C. Yu, M.Q. Lin, M. Bao et al., Empirical formulas for nuclear separation energies. Phys. Rev. C 100, 014314 (2019). https://doi.org/10.1103/PhysRevC.100.014314
S. Michimasa, M. Kobayashi, Y. Kiyokawa et al., Mapping of a new deformation region around \(^{62}\) Ti. Phys. Rev. Lett. 125, 122501 (2020). https://doi.org/10.1103/PhysRevLett.125.122501
M. Vilen, J. M. Kelly, A. Kankainen et al., Precision mass measurements on neutron-rich rare-earth isotopes at JYFLTRAP: Reduced neutron pairing and implications for \(r\) -process calculations. Phys. Rev. Lett. 120, 262701 (2018). https://doi.org/10.1103/PhysRevLett.120.262701
S. Lundberg, S.I. Lee, A unified approach to interpreting model predictions. arXiv:1705.07874 (2017)
P. Möller, A.J. Sierk, T. Ichikawa et al., Nuclear ground-state masses and deformations: FRDM(2012). Atomic Data Nucl. Data Tabels. 109–110, 1–204 (2016). https://doi.org/10.1016/j.adt.2015.10.002
G.L. Ke, Q. Meng, T. Finley et al., LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 3149–3157
Acknowledgements
Fruitful discussions with Prof. Jie Meng, Prof. Hong-Fei Zhang, Prof. Yu-Min Zhao, and Dr. Na-Na Ma are greatly appreciated. The authors acknowledge support by computing server C3S2 at the Huzhou University. The mass table for the LightGBM-refined mass models is available in the Supplemental Material.
Author information
Authors and Affiliations
Contributions
All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ze-Peng Gao, Yong-Jia Wang, Hong-Liang Lü, Qing-Feng Li, Cai-Wan Shen and Ling Liu. The first draft of the manuscript was written by Ze-Peng Gao, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. The contributions of Hong-Liang Lü are non-Huawei achievements.
Corresponding authors
Additional information
This work was supported in part by the National Science Foundation of China (Nos. U2032145, 11875125, 12047568, 11790323, 11790325, and 12075085), the National Key Research and Development Program of China (No. 2020YFE0202002), and the “Ten Thousand Talent Program” of Zhejiang Province (No. 2018R52017).
Supplementary Information
Below is the link to the electronic supplementary material.
Rights and permissions
About this article
Cite this article
Gao, ZP., Wang, YJ., Lü, HL. et al. Machine learning the nuclear mass. NUCL SCI TECH 32, 109 (2021). https://doi.org/10.1007/s41365-021-00956-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s41365-021-00956-1