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The construction of autonomous chemical laboratories is complex, laborious, and expensive. It is imperative to develop highly usable, flexible, and powerful development framework to unify efforts. Current platforms have high adoption barriers, whether through their high price of adoption, high complexity to implement, or limited implementation on diverse hardware and software systems. Furthermore, there are no studies to date which examine the practical usability of current platforms with state-of-the-art scientific studies. In this work we engineer a new, flexible laboratory automation platform (FLAB) to resolve these issues. With a unique, user-centric and agile approach, we develop this platform through eight different experimental studies, which represent the broad spectrum of activities in the integration of chemical laboratories and artificial intelligence. By beginning with a generalized, modular architecture in Python, each study created a generalized feature that evolved the platform’s utility and ease of use. Such features include advanced methods for synchronous processing, data manipulation and a user-interface for rapid prototyping. Ultimately, this study yields a simpler, more accessible and powerful toolset for the creation of autonomous chemical laboratories.
Jose et al. (Tue,) studied this question.