Optoelectronic properties of ultrasmall semiconductor nanocrystals are strongly related to their structural and microstructural features. However, due to the complexity of these materials, this intermingled relationship remains mostly elusive.

Over the past decades, total scattering methods, in particular the ones based on the Debye Scattering Equation (DSE) and operating in reciprocal space, have been established as essential tools for characterizing the structure, microstructure, and morphology of nanocrystals, including ultrasmall Quantum Dots (QDs).[1–5]

Although wide-angle scattering-based techniques are primarily sensitive to the atomic-scale structures of materials, reciprocal space total scattering methods provide robust information on multiple length scales, in particular if nanocrystalline materials are considered.

Nevertheless, constructing reliable, material-oriented atomistic models, to be optimized against the experimental data in order to extract structural and microstructural parameters remains a highly challenging task and often poses a bottleneck for scattering-based methods.[6–9]

To overcome this limitation, we tackle the challenge of developing reliable, efficient, and user-friendly methods for determining the average size of colloidal QDs, with a combination of reciprocal space total scattering methods based on DSE and an all-convolutional neural network (all-CNN) that provides physically interpretable results.

In this talk, I will present the development and first application of this novel tool to a selected class of lead-chalcogenide binary QDs that serves as a benchmark system. Indeed, they have been extensively characterized within the DSE approach, which has provided well-established knowledge about their structural and morphological features.[1]

The presented automated tool can be readily employed for real-time size classification of PbS QDs, even from diluted colloidal suspensions, within the limitations of the Q-range and signal-to-noise ratio typically encountered in in-situ and in-operando diffraction experiments.[10]

Additionally, it may serve as a rapid screening tool for the optimization of synthetic protocols.

Furthermore, the proposed method can be easily extended to other classes of nanocrystals, allowing non-experts in crystallography and X-ray diffraction to utilize the automated workflow for creating DSE pattern libraries used for training the all-CNN.