Also: GNoME, A-Lab, self-driving lab, robot
AI that predicts new crystals, and robots that run experiments on their own.
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Merchant and colleagues used graph networks trained at scale to discover 2.2 million structures below the known stability hull; 736 of the stable structures had already been made independently in experiments.
As reported in: Merchant, Batzner, Schoenholz et al. (2023). Scaling deep learning for materials discovery. Nature. doi:10.1038/s41586-023-06735-9
Szymanski and colleagues ran an autonomous lab that, over 17 days, made 41 of 58 targeted inorganic compounds using computation, literature data, machine learning and robotics.
Disputed: concerns were raised about structure identification and novelty; a 2026 author correction says the compounds were new to the prediction platform, not necessarily new to science (doi:10.1038/s41586-025-09992-y).
Publisher update notice: correction
As reported in: Szymanski, Rendy, Fei et al. (2023). An autonomous laboratory for the accelerated synthesis of inorganic materials. Nature. doi:10.1038/s41586-023-06734-w
Burger and colleagues used a free-roaming robot that ran 688 experiments over eight days and found photocatalyst mixtures six times more active than the starting ones.
As reported in: Burger, Maffettone, Gusev et al. (2020). A mobile robotic chemist. Nature. doi:10.1038/s41586-020-2442-2