Thin nanomaterials are key constituents of modern quantum technologies and materials research. The identification of specimens of these materials with the properties required for the development of state-of-the-art quantum devices is usually a complex and tedious 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 the classification of imperfect and 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.
Physical Review Applied
- Pub Date:
- June 2020
- Condensed Matter - Mesoscale and Nanoscale Physics;
- Condensed Matter - Materials Science;
- Physics - Computational Physics
- 8 pages, 4 figures