Nuclear masses are predicted with the Bayesian neural networks by learning the mass surface of even-even nuclei and the correlation energies to their neighboring nuclei. By keeping the known physics in various sophisticated mass models and performing the delicate design of neural networks, the proposed Bayesian machine learning mass model achieves an accuracy of $84\mathrm{keV}$, which crosses the accuracy threshold of the $100\mathrm{keV}$ in the experimentally known region. It is also demonstrated the corresponding uncertainties of mass predictions are properly evaluated, while the uncertainties increase by about $50\mathrm{keV}$ each step along the isotopic chains towards the unknown region. The shell structures in the known region are well described and several important features in the unknown region are predicted, such as the new magic numbers around $N=40$, the robustness of $N=82$ shell, the quenching of $N=126$ shell, and the smooth separation energies around $N=104$.

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• Binding energy & masses
• Nuclear astrophysics
• Nuclear binding
• Nuclear structure & decays
• Bayesian methods
• Machine learning
• Monte Carlo methods
• Nuclear density functional theory