  Thin nanomaterials are key constituents of modern quantum technologies and
materials research. Identifying specimens of these materials with properties
required for the development of state of the art quantum devices is usually a
complex and lengthy human task. In this work we provide a neural-network driven
solution that allows for accurate and efficient scanning, data-processing and
sample identification of experimentally relevant two-dimensional materials. We
show how to approach classification of imperfect imbalanced data sets using an
iterative application of multiple noisy neural networks. We embed the trained
classifier into a comprehensive solution for end-to-end automatized data
processing and sample identification.
