The morphology of nanoparticles governs their properties for a range of important applica tions. Thus, the ability to statistically correlate this key particle performance parameter is paramount
in achieving accurate control of nanoparticle properties. Among several effective techniques for
morphological characterization of nanoparticles, transmission electron microscopy (TEM) can pro vide a direct, accurate characterization of the details of nanoparticle structures and morphology at
atomic resolution. However, manually analyzing a large number of TEM images is laborious. In this
work, we demonstrate an efficient, robust and highly automated unsupervised machine learning
method for the metrology of nanoparticle systems based on TEM images. Our method not only can
achieve statistically significant analysis, but it is also robust against variable image quality, imaging
modalities, and particle dispersions. The ability to efficiently gain statistically significant particle
metrology is critical in advancing precise particle synthesis and accurate property control.Australia Research Council (ARC) IC210100056Ministerio de Economía y Competitividad TIN2014-55894-C2-RMinisterio de Economía y Competitividad TIN2017-88209-C2-2-
