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Combinatorial synthesis for AI-driven materials discovery

Nature Synthesis volume 2, pages 493–504 (2023)Cite this article

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Abstract

Combinatorial synthesis of solid-state materials comprises the use of automation or parallelization to systematically vary synthesis parameters. This approach to materials synthesis is a natural fit for accelerated mapping of composition–structure–property relationships, a central tenet of materials research. By considering combinatorial synthesis in the context of experimental workflows, we envision a future for accelerated materials science promoted by the co-development of combinatorial synthesis and artificial intelligence (AI) techniques. To evaluate the suitability of a synthesis technique for a given experimental workflow, we establish a collection of ten metrics spanning speed, scalability, scope and quality of synthesis. We summarize select combinatorial synthesis techniques in the context of these metrics, elucidating opportunities for further development. These opportunities span initial deployment in high-throughput experimentation through to seminal demonstrations of automated decision-making using AI. Historical analysis of combinatorial synthesis in the context of the Gartner hype cycle establishes a recent rise in productivity, indicating that the field is poised to realize accelerated materials science workflows that transform materials discovery and development.

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Fig. 1: Performance of combinatorial synthesis techniques with respect to ten metrics.
Fig. 2: The breadth of combinatorial synthesis via sputter deposition.
Fig. 3: Example implementation of inkjet printing for combinatorial synthesis of catalyst/semiconductor interfaces.
Fig. 4: Example microfluidic synthesis of compositionally varied CsPbX nanocrystals.
Fig. 5: Implementation of solution-based synthesis in an autonomous workflow.
Fig. 6: The trajectory of combinatorial synthesis.

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Acknowledgements

This material is primarily based on work performed by the Liquid Sunlight Alliance, which is supported by the US Department of Energy, Office of Science, Office of Basic Energy Sciences, Fuels from Sunlight Hub under award DE-SC0021266. The analysis of autonomous workflows was supported by the Air Force Office of Scientific Research under award FA9550-18-1-0136.

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  1. Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA

    John M. Gregoire, Lan Zhou & Joel A. Haber

  2. Liquid Sunlight Alliance, California Institute of Technology, Pasadena, CA, USA

    John M. Gregoire, Lan Zhou & Joel A. Haber

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J.M.G., L.Z. and J.A.H. planned the review and wrote the manuscript.

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Gregoire, J.M., Zhou, L. & Haber, J.A. Combinatorial synthesis for AI-driven materials discovery. Nat. Synth 2, 493–504 (2023). https://doi.org/10.1038/s44160-023-00251-4

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