  Defects dictate the properties of many functional materials. To understand
the behaviour of defects and their impact on physical properties, it is
necessary to identify the most stable defect geometries. However, global
structure searching is computationally challenging for high-throughput defect
studies or materials with complex defect landscapes, like alloys or disordered
solids. Here, we tackle this limitation by harnessing a machine-learning
surrogate model to qualitatively explore the defect structural landscape. By
learning defect motifs in a family of related metal chalcogenide and mixed
anion crystals, the model successfully predicts favourable reconstructions for
unseen defects in unseen compositions for 90% of cases, thereby reducing the
number of first-principles calculations by 73%. Using CdSe$_x$Te$_{1-x}$ alloys
as an exemplar, we train a model on the end member compositions and apply it to
find the stable geometries of all inequivalent vacancies for a range of mixing
concentrations, thus enabling more accurate and faster defect studies for
configurational complex systems.
