  The present article addresses an early-stage attempt on replacing the
analyticity-based sink strength terms in rate equations by surrogate models of
machine learning representation. Here we emphasise, in the context of
multiscale modelling, a combinative use of machine learning with scale
analysis, through which a set of fine-resolution problems of partial
differential equations describing the (quasi-steady) short-range individual
sink behaviour can be asymptotically sorted out from the mean-field kinetics.
Hence the training of machine learning is restrictively oriented, that is, to
express the local and already identified, but analytically unavailable
nonlinear functional relationships between the sink strengths and other local
continuum field quantities. With the trained models, one is enabled to
quantitatively investigate the biased effect shown by a void/bubble being a
point defect sink, and the results are compared with existing ones over
well-studied scenarios. Moreover, the faster diffusive mechanisms on
crystalline interfaces are distinguishingly modelled by locally planar rate
equations, and their linkages with rate equations for bulk diffusion are
formulated through derivative jumps of point defect concentrations across the
interfaces. Thus the distinctive role of crystalline interfaces as partial
sinks and quick diffusive channels can be investigated. Methodologicalwise, the
present treatment is also applicable for studying more complicated situation of
long-term sink behaviour observed in irradiated materials.
