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Flow-driven data intensification to accelerate autonomous inorganic materials discovery

Nature Chemical Engineering volume 2, pages 436–446 (2025)Cite this article

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Abstract

The rapid discovery of advanced functional materials is critical for overcoming pressing global challenges in energy and sustainability. Despite recent progress in self-driving laboratories and materials acceleration platforms, their capacity to explore complex parameter spaces is hampered by low data throughput. Here we introduce dynamic flow experiments as a data intensification strategy for inorganic materials syntheses within self-driving fluidic laboratories by the continuous mapping of transient reaction conditions to steady-state equivalents. Applied to CdSe colloidal quantum dots, as a testbed, dynamic flow experiments yield at least an order-of-magnitude improvement in data acquisition efficiency and reducing both time and chemical consumption compared to state-of-the-art self-driving fluidic laboratories. By integrating real-time, in situ characterization with microfluidic principles and autonomous experimentation, a dynamic flow experiment fundamentally redefines data utilization in self-driving fluidic laboratories, accelerating the discovery and optimization of emerging materials and creating a sustainable foundation for future autonomous materials research.

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Fig. 1: Experimental setup.
Fig. 2: DFE versus SSFE.
Fig. 3: DFE-assisted SDFL operation workflow.
Fig. 4: DFE sampling results.
Fig. 5: BO campaigns targeting different peak emission wavelengths of CdSe QDs.
Fig. 6: Continuous manufacturing of CdSe QDs.

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Data availability

All data supporting the findings of this study are available within the Article and its Supplementary Information. Source data are provided with this paper.

Code availability

The source code for the autonomous experimentation and digital twin models is available via GitHub at https://github.com/AbolhasaniLab in the ‘DynamicExperimentation’ repository.

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Acknowledgements

M.A. gratefully acknowledges financial support from the National Science Foundation (award nos. 1940959, 2315996 and 2420490) and the University of North Carolina Research Opportunities Initiative (UNC-ROI) program.

Author information

Author notes

  1. These authors contributed equally: Fernando Delgado-Licona, Abdulrahman Alsaiari, Hannah Dickerson.

Authors and Affiliations

  1. Department of Chemical & Biomolecular Engineering, North Carolina State University, Raleigh, NC, USA

    Fernando Delgado-Licona, Abdulrahman Alsaiari, Hannah Dickerson, Philip Klem, Arup Ghorai, Richard B. Canty, Jeffrey A. Bennett, Pragyan Jha, Nikolai Mukhin, Junbin Li, Sina Sadeghi, Fazel Bateni & Milad Abolhasani

  2. Tecnologico de Monterrey, School of Engineering and Sciences, Monterrey, Mexico

    Enrique A. López-Guajardo

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Contributions

F.D.-L. and M.A. conceived the project. F.B. and A.G. modified the precursor chemistry for flow synthesis. F.D.-L. and E.A.L.-G. designed the flow reactor geometry. F.D.-L., A.A., H.D. and M.A. designed and developed the flow synthesis platform. F.D.-L. programmed the automation and closed-loop experimentation protocols under the advisement of R.B.C. and J.A.B. F.D.-L., A.A., H.D., P.K. and A.G. conducted the experiments. F.D.-L. and A.A. conducted the data analysis under the advisement of M.A. F.D.-L., A.A., P.K. and N.M. prepared the data and setup visualization figures. P.J., N.M., J.L., A.G. and S.S conducted the ex situ characterization of the in-flow-synthesized samples. F.D.-L., A.A., H.D. and M.A. drafted the manuscript. M.A. acquired funding and directed the project. All authors provided feedback on the manuscript.

Corresponding author

Correspondence to Milad Abolhasani.

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Nature Chemical Engineering thanks Adam Clayton, Jason Moore and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Dynamic vs. Steady State Flow Experiments.

Two modes of data acquisition strategies for flow reactors for material synthesis are qualitatively compared: (a) traditional Steady State Flow Experiments (SSFE) and (b) Dynamic Flow Experiments (DFE). Dashed light gray frames represent an experiment, where the residence time is increased accordingly for each strategy. On top of each experiment frame, the collected data (sensor positioned at the end of the reactor) for each experiment is displayed. DFE captures all data during the continuous experiment, while SSFE captures data only at the end of the experiment when the steady state has been reached. Lastly, all data collected, from the start of the dynamic experiment (t = 0) to the end of the dynamic experiment (tf), can be mapped to steady-state residence time conditions, presented in both sections as the position inside the reactor that would experience the expected residence time in the reactor.

Supplementary information

Supplementary Information

Supplementary Sections 1–9, Figs. 1–9, Tables 1–5 and Discussion.

Source data

Source Data Fig. 1

Raw numerical data for all plots in Fig. 1.

Source Data Fig. 2

Raw numerical data for all plots in Fig. 2.

Source Data Fig. 4

Raw numerical data for all plots in Fig. 4.

Source Data Fig. 5

Raw numerical data for all plots in Fig. 5.

Source Data Fig. 6

Raw numerical data for all plots in Fig. 6.

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Delgado-Licona, F., Alsaiari, A., Dickerson, H. et al. Flow-driven data intensification to accelerate autonomous inorganic materials discovery. Nat Chem Eng 2, 436–446 (2025). https://doi.org/10.1038/s44286-025-00249-z

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