Autonomous and dynamic precursor selection for solid-state materials synthesis

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...

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Bibliographic Details
Published in:Nature communications 2023-10, Vol.14 (1), p.6956-13, Article 6956
Main Authors: Szymanski, Nathan J., Nevatia, Pragnay, Bartel, Christopher J., Zeng, Yan, Ceder, Gerbrand
Format: Article
Language:English
Subjects:
119/118
639/301/1034/1037
639/301/930/1032
Algorithms
Humanities and Social Sciences
Intermediates
MATERIALS SCIENCE
multidisciplinary
Optimization
Precursors
Science
Science (multidisciplinary)
Solid state
Synthesis
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Summary: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 (ARROWS 3 ) 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, ARROWS 3 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, ARROWS 3 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. Solid-state materials synthesis relies on effective precursor design. Here, the authors introduce an algorithm that combines ab-initio computations with insights gained from experimental outcomes to efficiently optimize the selection of precursors.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-42329-9