Machine Learning-Driven High-Precision Model for -Decay Energy and Half-Life Prediction of superheavy nuclei
This paper presents a Bayesian-optimized XGBoost model that accurately predicts the -decay energy and half-life of superheavy nuclei by integrating key nuclear structural features, outperforming traditional empirical models while providing physical insights through SHAP analysis.