<p>Materials science plays an important role in the field of fusion research. We focus on the binding energy of vacancy-type defects to screw dislocations. These defects produced by irradiation are known to affect the mechanical properties of the material. Traditional techniques, such as density functional theory or molecular dynamics simulations, can be used to study these defects. However, a combinatorial number of cases need to be analyzed to study the binding energy when several vacancies are present, which quickly becomes infeasible. To address this combinatorial issue, we present a neural network solution. From a subset of cases we can train a model, which in turn can predict the energy in a fraction of the time compared to traditional techniques. However, we have to deal with large uncertainties in our predictions. This is addressed by using uncertainty quantification techniques, such as mixture density networks. We present results for a large iron dataset and for a reduced tungsten dataset, in which our solution is shown to benefit from transfer learning. Therefore, we can use the model to analyze different materials while avoiding the cost of generating new large datasets and training the model from scratch. We see a mean absolute percentage error of 7.5% for the iron case and 9.6% for the reduced tungsten case.</p>