A computational and experimental survey of the Na8-xAxP2O9 framework reports a new phase, Na4SnP2O9, and identifies tunable chemistry, while machine-learned potential predictions of fast room-temperature conduction are not confirmed by experiment.
AlabOS: A Python-based Reconfigurable Workflow Management Framework for Autonomous Laboratories
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abstract
The recent advent of autonomous laboratories, coupled with algorithms for high-throughput screening and active learning, promises to accelerate materials discovery and innovation. As these autonomous systems grow in complexity, the demand for robust and efficient workflow management software becomes increasingly critical. In this paper, we introduce AlabOS, a general-purpose software framework for orchestrating experiments and managing resources, with an emphasis on automated laboratories for materials synthesis and characterization. AlabOS features a reconfigurable experiment workflow model and a resource reservation mechanism, enabling the simultaneous execution of varied workflows composed of modular tasks while eliminating conflicts between tasks. To showcase its capability, we demonstrate the implementation of AlabOS in a prototype autonomous materials laboratory, A-Lab, with around 3,500 samples synthesized over 1.5 years.
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cond-mat.mtrl-sci 1years
2025 1verdicts
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Synthetic accessibility and sodium ion conductivity of the Na$_{8-x}$A$^{x}$P$_2$O$_9$ (NAP) high-temperature sodium superionic conductor framework
A computational and experimental survey of the Na8-xAxP2O9 framework reports a new phase, Na4SnP2O9, and identifies tunable chemistry, while machine-learned potential predictions of fast room-temperature conduction are not confirmed by experiment.