用机器学习提升核结合能预测精度，误差低于35 keV。
Further exploration of binding energy residuals using machine learning and the development of a composite ensemble model
- 构建四模型集成框架，融合四种机器学习方法优化残差预测。
- 对非测量同位素及中子滴线附近区域的结合能预测误差均值仅34 keV。
- 最适合的模型是基于最小二乘的树集成，兼具良好内插与外推能力。
本文提出四模型树集成（FMTE）模型，由四个机器学习模型组合而成，基于原子质量评价（AME）2012的实验结合能数据训练。该模型可对所有质子数Z > 7、中子数N > 7的核素在AME 2020数据集上的结合能进行预测，标准差为76 keV，平均绝对偏差为34 keV。FMTE由三个新模型与一个已有模型组合而成，新模型采用四种机器学习方法对质量模型的结合能残差进行训练，并引入形状参数与其他物理特征。研究发现，最小二乘加权的树集成模型在残差预测上表现最优，具备优异的内插与外推能力。文中还对比了未测同位素的质量预测结果，并讨论了向中子滴线外推的可行性。
原文摘要 · Abstract (English)
This paper describes the development of the Four Model Tree Ensemble (FMTE). The FMTE is a composite of machine learning models trained on experimental binding energies from the Atomic Mass Evaluation (AME) 2012. The FMTE predicts binding energy values for all nuclei with N > 7 and Z > 7 from AME 2020 with a standard deviation of 76 keV and a mean average deviation of 34 keV. The FMTE model was developed by combining three new models with one prior model. The new models presented here have been trained on binding energy residuals from mass models using four machine learning approaches. The models presented in this work leverage shape parameters along with other physical features. We have determined the preferred machine learning approach for binding energy residuals is the least-squares boosted ensemble of trees. This approach appears to have a superior ability to both interpolate and extrapolate binding energy residuals. A comparison with the masses of isotopes that were not measured previously and a discussion of extrapolations approaching the neutron drip line have been included.
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