The discovery of nuclear fission in 1939 profoundly changed our understanding of nuclear physics. Many years of research have led to the development of nuclear fission models, from which evaluated nuclear data files are derived. These files are essential inputs to reactor simulations; yet, their quality needs to be improved.
FIFRELIN (FIssion FRagments Evaporation modeLINg) aimed to fill the gap between theoretical and experimental standpoints. This Monte Carlo, based on Hauser-Feshbach formalism, calculates the de-excitation path of a given nuclide. In this presentation, we will show the recent achievements and developments of FIFRELIN. We will first focus on angular momentum generation through gamma prompt, and isomeric ratio measurements and the role played by FIFRELIN in assessing such a complex quantity. Then, we will present some recent results on utilizing machine-learning techniques to improve the predictivity of FIFRELIN. Finally, we will discuss some extensions of FIFRELIN by modeling electron emission and multi-chance fission.