基于物理信息全连接神经网络的原子质量验证与外推
Validation and extrapolation of atomic mass with physics-informed fully connected neural network
AI总结：
本文比较三种改进神经网络用于原子质量验证与外推，采用宏观—微观建模并引入核物理约束，在 AME2020 及跨版本外推中显著提升精度并复现关键核现象。
摘要：
机器学习为验证和预测原子质量提供了一个强大的框架。我们比较了三种用于原子质量预测的表征与外推的改进神经网络方法。其中最强大的方法采用宏观—微观方法，并将复杂的核效应视为输出标签，在 AME2020 上取得了优异精度，在测试集中产生 0.122 MeV 的极低均方根偏差，显著低于其他替代方法。在由 AME2016 预测 AME2020 时，该方法还表现出更好的外推性能，均方根偏差为 0.191 MeV。我们进一步针对模型输入开展敏感性分析，以验证统计指标之外的可解释一致性。通过引入幻数和质量的理论预测，我们的全连接神经网络复现了包括核子配对关联和幻数效应在内的关键核现象。文中讨论了该框架的外推能力，并测试了预测同位素链新质量测量结果的准确性。
英文摘要：
Machine learning offers a powerful framework for validating and predicting atomic mass. We compare three improved neural network methods for representation and extrapolation for atomic mass prediction. The powerful method, adopting a macroscopic-microscopic approach and treating complex nuclear effects as output labels, achieves superior accuracy in AME2020, yielding a much lower root-mean-square deviation of 0.122 MeV in the test set, significantly lower than alternative methods. It also exhibits a better extrapolation performance when predicting AME2020 from AME2016, with a root-mean-square deviation of 0.191 MeV. We further conduct sensitivity analyses against the model inputs to verify interpretable alignment beyond statistical metrics. Incorporating theoretical predictions of magic numbers and masses, our fully connected neural networks reproduce key nuclear phenomena including nucleon pairing correlation and magic number effects. The extrapolation capability of the framework is discussed and the accuracy of predicting new mass measurements for isotope chains has also been tested.
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