Abstract
Semiconductor nanocrystals with uniform morphology and composition are expected to show consistent responses during light-matter interactions. However, microscopy reveals significant variations in their photoluminescence blinking patterns, even under identical experimental conditions. This discrepancy arises from differences in crystal defects and nonradiative trap states. As a result, heterogeneous blinking patterns serve as valuable indicator of material quality, uncovering several concealed features through statistical analysis of large datasets. Nonetheless, efficient segregation and analysis of numerous blinking trajectories remain a challenge due to laborious calculations, computational bottlenecks, and manual intervention. In this study, we introduce a robust unsupervised machine learning (UML) assisted module to cluster high-dimensional blinking patterns in near-real-time, while calculating category-wise power spectral densities (PSD) to investigate active traps. Furthermore, we explore the impact of data preprocessing on clustering performance. The ‘clustering-segregation-analysis’ (UML-PSD) methodology demonstrates versatility, paving a way to advance contemporary (micro)spectroscopy, specifically for rapid and cost-effective optical characterization of semiconductor nanocrystals.
Data availability
The trial blinking datasets, with and without multi-state blinking trajectories, can be found in the repository https://doi.org/10.5281/zenodo.1771595781.
Code availability
The codes are stored in the repository https://doi.org/10.5281/zenodo.17715957 (for UML analysis81) and https://zenodo.org/records/8027381 (for PSD analysis82). These codes can be universally utilized on any dataset of choice according to the application and interest of the user.
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Acknowledgements
R.R., K.S.M. and S.D.F. acknowledge financial support from the Research Foundation – Flanders (FWO grant numbers G0H2122N, EOS 40007495) and the KU Leuven Internal Funds (grant number C14/23/090). B.P. acknowledges Industrieel Onderzoeksfonds KU Leuven (IOF)-VTI-25-00160. S.S. acknowledges the support of Marie Skłodowska-Curie postdoctoral fellowship (No. 101151427, SPS_Nano) from the European Union’s Horizon Europe program, short stay abroad grant (K257023N) and travel grant (K147824N) from Research Foundation-Flanders (FWO). J.H. acknowledges financial support from the Research Foundation-Flanders (FWO grant numbers S002019N, G098319N, S004322N Gigapixel and G0AHQ25N), the KU Leuven Research Fund (iBOF-21-085 PERSIST), the Flemish government through long-term structural funding Methusalem (CASAS2, Meth/15/04), and the MPI as a fellow. E.D. acknowledges funding from the KU Leuven Internal Funds (grant numbers C14/23/090, CELSA/23/018) and FWO grant number G0AHQ25N, and the European Union (ERC Starting Grant, 101117274 X-PECT). However, the views and opinions expressed are those of the authors only and do not necessarily reflect those of the European Union or European Research Council. Neither the European Union nor the granting authority can be held responsible for them.
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Conceptualization: A.M., E.D., Methodology (Machine Learning): A.M., T.B., Methodology (Power spectral density): S.S., Wide-field imaging: A.M., L.C., Scanning tunneling spectroscopy: R.R., Sample preparation & characterization (CsPbBr3 system): B.P., H.J., C.S., M.P., L.L., Sample preparation & characterization (Alkyl-NDI SAMNs): R.R., A.T.R., Investigation & Visualization: A.M., Funding acquisition: E.D., J.H., Supervision: E.D., J.H., Writing – original draft: A.M., R.R., Writing—review & editing: K.S.M., S.S., B.P., S.D.F., M.R., E.W.M., J.H., E.D., #R.R. and B.P. contributed equally.
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Mukherjee, A., Reynaerts, R., Pradhan, B. et al. Machine learning for microscopy data analytics targeting real-time optical characterization of semiconductor nanocrystals. Nat Commun (2026). https://doi.org/10.1038/s41467-026-68939-7
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DOI: https://doi.org/10.1038/s41467-026-68939-7
