# Modelling the Defect Processes of Materials for Energy Applications
## 5 Machine Learning Approaches for Defect Prediction  
Recent advances in machine learning [131] have been exploited to discover and design of new materials [132] and to solve a large range of predictive problems in materials' defect processes [133; 134], which are important for the structural integrity assessment of system components and effective scheduling of maintenance work, in application areas that are safety-critical and require uninterrupted and reliable operation. Typically, machine learning models are constructed by training them on known datasets, which may include actual or simulated data. The models are subsequently exposed to hitherto unseen (new) data about components that are exposed to conditions similar to those of the training set, and they produce predictions, typically in the form of interpolation or extrapolation depending on the specifics of the application under study. For instance, [PERSON] et al. [135] employ neural networks to predict irradiation embrittlement in low-alloy ferritic steel forgings and welds, which are typically used in most reactor pressure vessels of modern light-water nuclear reactors. Irradiation embrittlement causes the formation of many atomic-scale defects within the metal grains and such defects impede the motion of dislocations, leading to an increase in the hardness and a reduction in ductility of steel. Predicting irradiation embrittlement is critical for the safe and continued operation of the reactor. The research was based on two separate datasets, namely the IVAR data from the Irradiation Variables Experiment [136] and data from the US civil power database (NUREG) [137], which contains the accumulated experience from irradiated surveillance samples from US operators, and built a feed-forward multilayer perceptron (MLP) [138]. The main challenge for irradiation embrittlement forecasting is to establish the correct correlations between the many exposure parameters. Irradiation temperature is one of the most important of these parameters, and although it is feasible to assimilate data on the embrittlement of steels at many temperatures, to predict the actual temperature dependence of embrittlement in a particular steel is hard because of the effect of temperature on other variables such as irradiation flux and the composition of the steel itself. The predictions produced by the ANN that was developed in this study come with high uncertainty; however, once the model is set up, it is relatively easy to retrain it (and improve its performance) as more surveillance data become available. In a similar spirit, [139] explores the application of probabilistic kernel machines, specifically Gaussian processes, for weld stress prediction, using 16 different kernel functions for prediction. The aim of that work is to produce predictions that would be useful for the a priori scheduling of maintenance for stainless steel pipes, which are critical components for the safe and uninterrupted operation of power plants. Kernel machines are employed in pattern analysis to identify general types of relations (for example, clusters, classifications, principal components) in datasets. The use of kernel functions enables these methods to work in the implicit feature space, without computing the coordinates of data points in that space; hence this \"kernel trick\" affords cheaper computation. A Gaussian process is a kernel machine with a multivariate normal distribution, and can be seen as the distribution over functions with a continuous domain. The approach was tested using the dataset offered in [140], whose measurements were obtained using various techniques such as deep hole drilling, neutron diffraction and block removal splitting and layering as part of a UK nuclear power industry research programme. The work in [141] compares different data-driven approaches, specifically artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs), for the prediction of residual stress in stainless steel welds found in pressure vessels and pipe systems of energy systems.