A general scheme for point defect sink strength calculation and related machine-learning-based expressions

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Irradiation tends to increase the concentration of point defects (PDs) in crystalline materials, whose consecutive interactions with other types of defects, such as dislocation and void, are recognised highly responsible for the characteristic plastic and damaging behaviours of materials under irradiation. Conventional treatments on evaluating the strength of PD sinks see their limitation with strong regularity requirements over the models used for summarising the key underlying microstructural behaviours, where analytical solutions are bound to be the outcome. The present article serves to introduce a general scheme for PD sink strength evaluation, where constraints on solution analyticity are fully resolved with the use of machine learning. In particular, a neural network representation of the PD sink strength due to void/bubble is derived, where PD transportation tendencies against the hydrostatic pressure gradient surrounding a bubble can be considered in details. The treatment is also applied to analyse PD sink strength due to edge dislocation clusters. For nearly uniformly distributed clusters, upon undertaking a two-scale asymptotic strategy, the corresponding sink strength formulation becomes explicit. For randomly distributed dislocations, the sink strength is found to roughly scale with the onsite dislocation density. But for patterned dislocations, such as dislocation dipoles, their sink strength is suggested to vary with the applied load. The machine-learning-based formulation is also compared well with the results obtained by other multiscale methods.