Amrani, NaimaYesilkanat, Cafer MertAkkoyun, Serkan2025-05-042025-05-0420240164-03131572-9591https://doi.org/10.1007/s10894-024-00461-4https://hdl.handle.net/20.500.12418/35611This 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, alpha) 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.en10.1007/s10894-024-00461-4info:eu-repo/semantics/closedAccessReaction cross-section(n, alpha) reactionMachine-learningArticle4322-s2.0-85206250355Q2WOS:001337155400001Q1