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Liquid drop model accuracy is optimized 80% by Bayesian deep neural network (BDNN) to calculate the known nuclei binding energies and also used to predicate extra unknown nucleus. In this paper, KL(Kullback-Leibler) divergence from BDNN is adopted and further optimized by the variational reasoning method. The latest atomic data (AME 2020) is taken as input to train the BDNN, the root means square(RMS) of 2457 types known nuclei (
$Z\geqslant 8$ and$N\geqslant 8$ ) calculation is improved 80% (from 2.9894 MeV to 0.5695 MeV). Additionally, we improved the input of BDNN in this work, so that the unknown nucleus ( Z= 118–126) can be limited in a region(Regional restriction strategy), which improves the stability of prediction. Experimental data (nuclei Z= 100–117) also match well with our prediction and showed this calculation method is promising. The further binding energy for proton numbers from 118–126 is predicate using our method.-
Keywords:
- binding energy/
- BDNN/
- liquid drop model/
- regional restriction strategy
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系数名称 $a_{ {\rm{v} } }/{\rm{MeV}}$ $ a_{{\rm{s}}}/{\rm{MeV}} $ $ k_{{\rm{v}}} $ $ k_{{\rm{s}}} $ $ a_{{\rm{c}}}/{\rm{MeV}} $ $ f_{{\rm{p}}}/{\rm{MeV}} $ $ d_{{\rm{n}}}/{\rm{MeV}} $ $ d_{{\rm{p}}}/{\rm{MeV}} $ $ d_{{\rm{np}}}/{\rm{MeV}} $ 系数值 –15.4299 18.0212 –1.6193 –0.8138 0.7034 –1.4511 5.6149 5.5802 –4.9721 误差 0.0001 0.0001 0.0011 0.0010 0.0001 0.0014 0.1854 0.1321 1.100 
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训练集(80%) 预测集(20%) 全局
计算${\rm{LDM} }_{\sigma_{ {\rm{pre} } }/{\rm{MeV}}}$ 3.0114 2.9374 2.9894 ${\rm{LDM} }+{\rm{BDNN} }_{\sigma_{ {\rm{post} } }/{\rm{MeV}}}$ 0.5675 0.5565 0.5695 DownLoad:
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模型 LSD FRDM FRDM12 TF HFB21 GHFB KTUY Bhagwat LMNN LDM+BDNN 数据年份 AME2012 AME2012 AME2012 AME2012 AME2012 AME2012 AME2012 AME2012 AME2012 AME2020 $N_{{\rm{nucl}}}$ 2316 2353 2353 2353 2353 2353 2353 2353 2353 2457 $\sigma_{{\rm{rms}}}$/MeV 0.608 0.654 0.579 0.649 0.572 0.789 0.701 0.266 0.235 0.5695 DownLoad:
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[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] [44] [45]
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