Chirality Analysis for Nanostructured Microparticles Using Deep Learning

Chirality of helical objects, exemplified by nanostructured inorganic particles, has unifying importance for many scientific fields. Their handedness can be determined visually, but its identification by analysis of electron microscopy images is fundamentally difficult because (1) image features differentiating left- and right-handed particles can be ambiguous and ancillary, and (2) three-dimensional particle structure essential for chirality is 'flattened' into two-dimensional projections. Here we show that deep learning algorithms can reliably identify and classify twisted bowtie-shaped microparticles in scanning electron microscopy images with accuracy as high as 94.4% having been trained on as few as 180 images. Furthermore, after training on bowtie particles with complex nanostructured features, the model can recognize other chiral shapes with different geometries without re-training. These findings indicate that deep learning can potentially replicate the visual analysis of chiral objects by humans and enable automated analysis of microscopy data for the accelerated discovery of chiral materials.