Orchestrating nimble experiments across interconnected labs

Abstract

Advancements in artificial intelligence (AI) for science are continually expanding the value proposition for automation in materials and chemistry experiments. The advent of hierarchical decision-making also motivates automation of not only the individual measurements but also the coordination among multiple research workflows. In a typical lab or network of labs, workflows need to independently start and stop operation while also sharing resources such as centralized or multi-functional equipment. A new paradigm in instrument control is needed to realize the combination of independence with respect to periods of operation and interdependence with respect to shared resources. We present Hierarchical Experimental Laboratory Automation and Orchestration with asynchronous programming (HELAO-async), which is implemented via the Python asyncio package by abstracting each resource manager and experiment orchestrator as a FastAPI server. This framework enables coordinated workflows of adaptive experiments, which will elevate Materials Acceleration Platforms (MAPs) from islands of accelerated discovery to the AI emulation of team science.

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