Authors: Federica Bertolotti | Lucia Allara | Antonietta Guagliardi
Source:  (article, 2023-12-18)
Field: Materials Science — Materials Chemistry

Deep learning for the efficient size classification of quantum dots from total scattering data Deep learning for the efficient size classification of quantum dots from total scattering dataFederica Bertolotti a, Lucia Allara a, Antonietta Guagliardi ba Dipartimento di Scienza e Alta Tecnologia & To.Sca.Lab, Università dell'Insubria, via Valleggio 11, 22100 Como, Italyb Istituto di Cristallografia & To.Sca.Lab, Consiglio Nazionale delle Ricerche, via Valleggio 11, 22100 Como, ItalyMaterials for Sustainable Development Conference (MATSUS)Proceedings of MATSUS Spring 2024 Conference (MATSUS24)#AI - Automation and Nanomaterials (machine learning, artificial intelligence, robotics, accelerated discovery)Barcelona, Spain, 2024 March 4th - 8thOrganizers: Ivan Infante and Oleksandr VoznyyInvited Speaker, Federica Bertolotti, presentation 176DOI: https://doi.org/10.29363/nanoge.matsus.2024.176Publication date: 18th December 2023Optoelectronic 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. References:[1] F. Bertolotti, D. N. Dirin, M. Ibáñez, F. Krumeich, A. Cervellino, R. Frison, O. Voznyy, E. H. Sargent, M. V. Kovalenko, A. Guagliardi, N. Masciocchi, Nat. Mater. 2016, 15, 987.[2] F. Bertolotti, L. Protesescu, M. V. Kovalenko, S. Yakunin, A. Cervellino, S. J. L. Billinge, M. W. Terban, J. S. Pedersen, N. Masciocchi, A. Guagliardi, ACS Nano 2017, 11, 3819.[3] F. Bertolotti, G. Nedelcu, A. Vivani, A. Cervellino, N. Masciocchi, A. Guagliardi, M. V. Kovalenko, ACS Nano 2019, 13, 14294.[4] F. Bertolotti, A. Vivani, F. Ferri, P. Anzini, A. Cervellino, M. I. Bodnarchuk, G. Nedelcu, C. Bernasconi, M. V. Kovalenko, N. Masciocchi, A. Guagliardi, Chem. Mater. 2022, 34, 594.[5] D. Moscheni, F. Bertolotti, L. Piveteau, L. Protesescu, D. N. Dirin, M. V. Kovalenko, A. Cervellino, J. S. Pedersen, N. Masciocchi, A. Guagliardi, ACS Nano 2018, 12, 12558.[6] A. S. Anker, E. T. S. Kjær, M. Juelsholt, T. L. Christiansen, S. L. Skjærvø, M. R. V. Jørgensen, I. Kantor, D. R. Sørensen, S. J. L. Billinge, R. Selvan, K. M. Ø. Jensen, npj Comput Mater 2022, 8, 1.[7] F. Oviedo, Z. Ren, S. Sun, C. Settens, Z. Liu, N. T. P. Hartono, S. Ramasamy, B. L. DeCost, S. I. P. Tian, G. Romano, A. Gilad Kusne, T. Buonassisi, npj Comput Mater 2019, 5, 1.[8] N. J. Szymanski, C. J. Bartel, Y. Zeng, Q. Tu, G. Ceder, Chem. Mater. 2021, 33, 4204.[9] N. J. Szymanski, C. J. Bartel, Y. Zeng, M. Diallo, H. Kim, G. Ceder, npj Comput Mater 2023, 9, 1.[10] L. Allara, F.Bertolotti, A. Guagliardi, ChemRxivAcknowledgements:The Italian Ministry for Universities and Research (MUR) is acknowledged for partial funding (PRIN-20223CMHRZ) © FUNDACIO DE LA COMUNITAT VALENCIANA SCITOnanoGe is a prestigious brand of successful science conferences that are developed along the year in different areas of the world since 2009. 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