α-decay half-life predictions with support vector machine

In this study, we investigate the application of support vector machines utilizing a radial basis function kernel for predicting nuclear α-decay half-lives. Our approach integrates a comprehensive set of physics-derived features, including characteristics derived from nuclear structure, to systematically evaluate their impact on predictive accuracy. In addition to traditional parameters such as proton and neutron numbers, as well as terms based on the liquid drop model (e.g., volume, surface, Coulomb features), we incorporate decay energies and orbital angular momentum quantum numbers for both parent and daughter nuclei. Our analysis of 2232 nuclear data points demonstrates that the use of the radial basis function kernel yields predictive models with root mean square errors of 0.819 (for set1) and 0.352 (for set2), aligning with results obtained from comparable machine learning methodologies. Furthermore, Shapley additive explanations values highlight the predominant role of parent nuclei in predicting α-decay half-lives within the support vector machines. These results highlight the effectiveness of machine learning in nuclear structure research, opening up new possibilities for predicting the α-decay half-lives of previously unstudied nuclei.