An autonomous laboratory for the accelerated synthesis of novel materials

Nathan J. Szymanski, Bernardus Rendy, Yuxing Fei, Rishi E. Kumar, Tanjin He, David Milsted, Matthew J. McDermott, Max Gallant, Ekin Dogus Cubuk, Amil Merchant, Haegyeom Kim, Anubhav Jain, Christopher J. Bartel, Kristin Persson, Yan Zeng () and Gerbrand Ceder ()
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Nathan J. Szymanski: University of California, Berkeley
Bernardus Rendy: University of California, Berkeley
Yuxing Fei: University of California, Berkeley
Rishi E. Kumar: Lawrence Berkeley National Laboratory
Tanjin He: University of California, Berkeley
David Milsted: Lawrence Berkeley National Laboratory
Matthew J. McDermott: University of California, Berkeley
Max Gallant: University of California, Berkeley
Ekin Dogus Cubuk: Google DeepMind
Amil Merchant: Google DeepMind
Haegyeom Kim: Lawrence Berkeley National Laboratory
Anubhav Jain: Lawrence Berkeley National Laboratory
Christopher J. Bartel: Lawrence Berkeley National Laboratory
Kristin Persson: University of California, Berkeley
Yan Zeng: Lawrence Berkeley National Laboratory
Gerbrand Ceder: University of California, Berkeley

Nature, 2023, vol. 624, issue 7990, 86-91

Abstract: Abstract To close the gap between the rates of computational screening and experimental realization of novel materials1,2, we introduce the A-Lab, an autonomous laboratory for the solid-state synthesis of inorganic powders. This platform uses computations, historical data from the literature, machine learning (ML) and active learning to plan and interpret the outcomes of experiments performed using robotics. Over 17 days of continuous operation, the A-Lab realized 41 novel compounds from a set of 58 targets including a variety of oxides and phosphates that were identified using large-scale ab initio phase-stability data from the Materials Project and Google DeepMind. Synthesis recipes were proposed by natural-language models trained on the literature and optimized using an active-learning approach grounded in thermodynamics. Analysis of the failed syntheses provides direct and actionable suggestions to improve current techniques for materials screening and synthesis design. The high success rate demonstrates the effectiveness of artificial-intelligence-driven platforms for autonomous materials discovery and motivates further integration of computations, historical knowledge and robotics.

Date: 2023
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DOI: 10.1038/s41586-023-06734-w

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