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本文基于梯度提升决策树(Gradient Boosting Decision Tree,GBDT)的机器学习算法，构建了一种用于预测99-103Mo*熔合反应截面(Cross Section,CS)的模型，旨在探索医用同位素99Mo的最优合成路径。模型输入包括反应能量、质子数、质量数及结合能等特征量，以及基于唯象理论模型计算的相关参数，输出量为熔合反应截面。研究发现，在测试集上机器学习预测的CS与实验值的平均绝对误差(Mean Absolute Error，MAE)为0.0615，优于EBD2模型预测的0.1103。在此基础上，结合GEMINI++程序计算了100-103Mo*的中子衰变道的存活几率进而得到99Mo的蒸发剩余截面,发现4He+97Zr在质心能量为18.51 MeV时的2n退激反应道的蒸发剩余截面为1199.80 mb,是合成99Mo的最优路径。该研究验证了基于物理信息的机器学习方法在熔合反应截面预测中的可靠性，可为优化反应体系选择及在重离子加速器上通过熔合反应产生医用同位素提供参考。本文数据集可在科学数据银行数据库https://doi.org/10.57760/sciencedb.j00213.00244中访问获取(审稿阶段请通过私有访问链接查看本文数据集https://www.scidb.cn/s/zU3Yja)。Based on the Gradient Boosting Decision Tree (GBDT) machine learning algorithm, this study develops a model for predicting the fusion reaction cross-section (CS) of 99-103Mo*, aiming to explore the optimal synthesis pathway for the medical isotope 99Mo. The model inputs include characteristic quantities such as reaction energy, proton number, mass number, and binding energy, as well as relevant parameters calculated based on phenomenological theoretical models, with the output being the fusion reaction cross-section. It is found that the mean absolute error (MAE) between the machine learning-predicted CS and experimental values on the test set is 0.0615, which is superior to the 0.1103 predicted by the EBD2 model. On this basis, combined with the GEMINI++ program, the survival probabilities of the neutron decay channels for 99-103Mo* were calculated to derive the evaporation residue cross-section of 99Mo. It is found that the evaporation residue cross-section of the 2n de-excitation channel for 4He+97Zr at a center-of-mass energy of 18.51 MeV is 1199.80 mb, making it the optimal pathway for synthesizing 99Mo. This research validates the reliability of physics-informed machine learning methods in predicting fusion reaction cross-sections and provides a reference for optimizing reaction system selection and producing medical isotopes through fusion reactions in heavy-ion accelerators.
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Keywords:
- Medical Isotope Production /
- Heavy-Ion Fusion Reactions /
- Capture Cross Sections /
- Machine Learning
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