AutoDetect-mNP: An Unsupervised Machine Learning Algorithm for Automated Analysis of Transmission Electron Microscope Images of Metal Nanoparticles

Abstract

The synthesis quality of artificial inorganic nanocrystals is most often assessed by transmission electron microscopy (TEM) for which new high-throughput advances have dramatically increased both the quantity and information richness of metal nanoparticle (mNP) characterization. Existing automated data analysis algorithms of TEM mNP images generally adopt a supervised approach, requiring a significant effort in human preparation of labelled data that reduces objectivity, efficiency, and generalizability. We have developed an unsupervised algorithm AutoDetect-mNP for automated analysis of TEM images that objectively extracts morphological information of convex mNPs from TEM images based on their shape attributes, requiring little to no human input in the process. The performance of AutoDetect-mNP is tested on two datasets of bright field TEM images of Au nanoparticles with different shapes, demonstrating that the algorithm is quantitatively reliable, and can thus serve as a generalizable measure of the morphology distributions of any mNP synthesis. The AutoDetect-mNP algorithm will aid in future developments of high-throughput characterization of newly synthesized mNPs, and the future advent of time-resolved TEM studies that can investigate reaction mechanisms of mNP synthesis and reactivity.