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An autonomous laboratory for the accelerated synthesis of novel materials.

ABSTRACT: To close the gap between the rates of computational screening and experimental realization of novel materials^1,2, 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.

SUBMITTER: Szymanski NJ

PROVIDER: S-EPMC10700133 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature

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An autonomous laboratory for the accelerated synthesis of novel materials.

Szymanski Nathan J NJ Rendy Bernardus B Fei Yuxing Y Kumar Rishi E RE He Tanjin T Milsted David D McDermott Matthew J MJ Gallant Max M Cubuk Ekin Dogus ED Merchant Amil A Kim Haegyeom H Jain Anubhav A Bartel Christopher J CJ Persson Kristin K Zeng Yan Y Ceder Gerbrand G

Nature 20231129 7990

To close the gap between the rates of computational screening and experimental realization of novel materials<sup>1,2</sup>, 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 ...[more]

PMID: 38030721

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Project description:BackgroundThe demand for biobased polymers is increasing steadily worldwide. Microbial hosts for production of their monomeric precursors such as glutarate are developed. To meet the market demand, production hosts have to be improved constantly with respect to product titers and yields, but also shortening bioprocess duration is important.ResultsIn this study, adaptive laboratory evolution was used to improve a C. glutamicum strain engineered for production of the C5-dicarboxylic acid glutarate by flux enforcement. Deletion of the L-glutamic acid dehydrogenase gene gdh coupled growth to glutarate production since two transaminases in the glutarate pathway are crucial for nitrogen assimilation. The hypothesis that strains selected for faster glutarate-coupled growth by adaptive laboratory evolution show improved glutarate production was tested. A serial dilution growth experiment allowed isolating faster growing mutants with growth rates increasing from 0.10 h-1 by the parental strain to 0.17 h-1 by the fastest mutant. Indeed, the fastest growing mutant produced glutarate with a twofold higher volumetric productivity of 0.18 g L-1 h-1 than the parental strain. Genome sequencing of the evolved strain revealed candidate mutations for improved production. Reverse genetic engineering revealed that an amino acid exchange in the large subunit of L-glutamic acid-2-oxoglutarate aminotransferase was causal for accelerated glutarate production and its beneficial effect was dependent on flux enforcement due to deletion of gdh. Performance of the evolved mutant was stable at the 2 L bioreactor-scale operated in batch and fed-batch mode in a mineral salts medium and reached a titer of 22.7 g L-1, a yield of 0.23 g g-1 and a volumetric productivity of 0.35 g L-1 h-1. Reactive extraction of glutarate directly from the fermentation broth was optimized leading to yields of 58% and 99% in the reactive extraction and reactive re-extraction step, respectively. The fermentation medium was adapted according to the downstream processing results.ConclusionFlux enforcement to couple growth to operation of a product biosynthesis pathway provides a basis to select strains growing and producing faster by adaptive laboratory evolution. After identifying candidate mutations by genome sequencing causal mutations can be identified by reverse genetics. As exemplified here for glutarate production by C. glutamicum, this approach allowed deducing rational metabolic engineering strategies.

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An autonomous laboratory for the accelerated synthesis of novel materials.

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An autonomous laboratory for the accelerated synthesis of novel materials.

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