Amrani, NaimaYeşilkanat, Cafer MertAkkoyun, Serkan2024-12-022024-12-0220240164-0313http://dx.doi.org/10.1007/s10894-024-00461-4https://hdl.handle.net/11494/5089This study focuses on leveraging powerful machine learning approaches to determine neutron- alpha reaction cross-sections within the 14–15 MeV energy range. The investigation utilizes an experimental dataset comprising measurements of 133 nuclei concerning (n, ?) reaction cross- sections. These data are divided into training and validation subsets, following established protocols, with 80% allocated for model training and 20% for testing. Key nucleus characteristics, including neutron number (N), mass number (A), and symmetry representation [(N-Z)²/A], were used as input variables for the machine learning models. SVR and XGBoost methods showed superior performance among the other machine learning methods used in the present study. In addition, a machine learning based online calculation tool was developed to estimate the reaction cross section.eninfo:eu-repo/semantics/embargoedAccess(n, α) ReactionMachine-LearningReaction Cross-SectionArticle43210.1007/s10894-024-00461-4Q2Q1