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Autonomous and dynamic precursor selection for solid-state materials synthesis

Author

Listed:
  • Nathan J. Szymanski

    (UC Berkeley
    Lawrence Berkeley National Laboratory)

  • Pragnay Nevatia

    (UC Berkeley)

  • Christopher J. Bartel

    (University of Minnesota)

  • Yan Zeng

    (UC Berkeley)

  • Gerbrand Ceder

    (UC Berkeley
    Lawrence Berkeley National Laboratory)

Abstract

Solid-state synthesis plays an important role in the development of new materials and technologies. While in situ characterization and ab-initio computations have advanced our understanding of materials synthesis, experiments targeting new compounds often still require many different precursors and conditions to be tested. Here we introduce an algorithm (ARROWS3) designed to automate the selection of optimal precursors for solid-state materials synthesis. This algorithm actively learns from experimental outcomes to determine which precursors lead to unfavorable reactions that form highly stable intermediates, preventing the target material’s formation. Based on this information, ARROWS3 proposes new experiments using precursors it predicts to avoid such intermediates, thereby retaining a larger thermodynamic driving force to form the target. We validate this approach on three experimental datasets, containing results from over 200 synthesis procedures. In comparison to black-box optimization, ARROWS3 identifies effective precursor sets for each target while requiring substantially fewer experimental iterations. These findings highlight the importance of domain knowledge in optimization algorithms for materials synthesis, which are critical for the development of fully autonomous research platforms.

Suggested Citation

  • Nathan J. Szymanski & Pragnay Nevatia & Christopher J. Bartel & Yan Zeng & Gerbrand Ceder, 2023. "Autonomous and dynamic precursor selection for solid-state materials synthesis," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42329-9
    DOI: 10.1038/s41467-023-42329-9
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    References listed on IDEAS

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    1. Benjamin J. Shields & Jason Stevens & Jun Li & Marvin Parasram & Farhan Damani & Jesus I. Martinez Alvarado & Jacob M. Janey & Ryan P. Adams & Abigail G. Doyle, 2021. "Bayesian reaction optimization as a tool for chemical synthesis," Nature, Nature, vol. 590(7844), pages 89-96, February.
    2. Marwin H. S. Segler & Mike Preuss & Mark P. Waller, 2018. "Planning chemical syntheses with deep neural networks and symbolic AI," Nature, Nature, vol. 555(7698), pages 604-610, March.
    3. Matthew J. McDermott & Shyam S. Dwaraknath & Kristin A. Persson, 2021. "A graph-based network for predicting chemical reaction pathways in solid-state materials synthesis," Nature Communications, Nature, vol. 12(1), pages 1-12, December.
    4. A. Gilad Kusne & Heshan Yu & Changming Wu & Huairuo Zhang & Jason Hattrick-Simpers & Brian DeCost & Suchismita Sarker & Corey Oses & Cormac Toher & Stefano Curtarolo & Albert V. Davydov & Ritesh Agarw, 2020. "On-the-fly closed-loop materials discovery via Bayesian active learning," Nature Communications, Nature, vol. 11(1), pages 1-11, December.
    5. Christopher J. Bartel & Samantha L. Millican & Ann M. Deml & John R. Rumptz & William Tumas & Alan W. Weimer & Stephan Lany & Vladan Stevanović & Charles B. Musgrave & Aaron M. Holder, 2018. "Physical descriptor for the Gibbs energy of inorganic crystalline solids and temperature-dependent materials chemistry," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    6. Paul Raccuglia & Katherine C. Elbert & Philip D. F. Adler & Casey Falk & Malia B. Wenny & Aurelio Mollo & Matthias Zeller & Sorelle A. Friedler & Joshua Schrier & Alexander J. Norquist, 2016. "Machine-learning-assisted materials discovery using failed experiments," Nature, Nature, vol. 533(7601), pages 73-76, May.
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    Cited by:

    1. Bin Ouyang & Yan Zeng, 2024. "The rise of high-entropy battery materials," Nature Communications, Nature, vol. 15(1), pages 1-5, December.

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