Neural network analysis of Charpy transition temperature of irradiated low-activation martensitic steels

We have constructed a Bayesian neural network model that predicts the change, due to neutron irradiation, of the Charpy ductile-brittle transition temperature (ΔDBTT) of low-activation martensitic steels given a set of multi-dimensional published data with doses <100 displacements per atom (dpa). Results show the high significance of irradiation temperature and (dpa)1/2 in determining ΔDBTT. Sparse data regions were identified by the size of the modelling uncertainties, indicating areas where further experimental data are needed. The method has promise for selecting and ranking experiments on future irradiation materials test facilities.