Simulated HRTEM images of nanoparticles to train a neural network to classify nanoparticles for crystallinity

Abstract

Machine learning approaches for image analysis require extensive training datasets for an accurate analysis. This also applies to the automated analysis of electron microscopy data where training data are usually created by manual annotation. Besides nanoparticle shape and size distribution, their internal crystal structure is a major parameter to assess their nature and their physical properties. The automatic classification of ultrasmall gold nanoparticles (1–3 nm) by their crystallinity is possible after training a neural network with simulated HRTEM data. This avoids a human bias and the necessity to manually classify extensive particle sets as training data. The small size of these particles represents a significant challenge with respect to the question of internal crystallinity. The network was able to assign real particles imaged by HRTEM with high accuracy to the classes monocrystalline, polycrystalline, and amorphous after being trained with simulated datasets. The ability to adjust the simulation parameters opens the possibility to extend this procedure to other experimental setups and other types of nanoparticles.