Enabling Modular Autonomous Feedback-Loops in Materials Science through Hierarchical Experimental Laboratory Automation and Orchestration
Materials acceleration platforms (MAPs) operate on the paradigm of integrating combinatorial synthesis, high-throughput characterization, automatic analysis, and machine learning. Within a MAP, one or multiple autonomous feedback loops may aim to optimize materials for certain functional properties or to generate new insights. The scope of a given experiment campaign is defined by the range of experiment and analysis actions that are integrated into the experiment framework. Herein, the authors present a method for integrating many actions within a hierarchical experimental laboratory automation and orchestration (HELAO) framework. They demonstrate the capability of orchestrating distributed research instruments that can incorporate data from experiments, simulations, and databases. HELAO interfaces laboratory hardware and software distributed across several computers and operating systems for executing experiments, data analysis, provenance tracking, and autonomous planning. Parallelization is an effective approach for accelerating knowledge generation provided that multiple instruments can be effectively coordinated, which the authors demonstrate with parallel electrochemistry experiments orchestrated by HELAO. Efficient implementation of autonomous research strategies requires device sharing, asynchronous multithreading, and full integration of data management in experimental orchestration, which to the best of the authors' knowledge, is demonstrated for the first time herein.
Additional Information© 2022 The Authors. Advanced Materials Interfaces published by Wiley-VCH GmbH. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. Issue Online: 11 March 2022; Version of Record online: 06 January 2022; Manuscript revised: 21 November 2021; Manuscript received: 12 October 2021. This work contributes to the research performed at CELEST (Center for Electrochemical Energy Storage Ulm-Karlsruhe) and was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany´s Excellence Strategy – EXC 2154 – Project number 390874152. This project has also received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No 957189. Design of software architecture at Caltech was supported by the Liquid Sunlight Alliance, which is supported by the U.S. Department of Energy (DOE), Office of Science, Office of Basic Energy Sciences (BES), Fuels from Sunlight Hub under Award Number DE-SC0021266. The authors would like to thank Ephraim Schoof for developing the KaDI4Mat API and helping in the initial interfacing with data management. They would like to thank KIT IAM for hosting KaDI4Mat. Open access funding enabled and organized by Projekt DEAL. Author Contributions: H.S.S. and J.M.G conceived the idea and designed the first software layout. H.S.S. developed the first drivers and server-based communication protocols. F.R., J.F., D.G., M.R., and P.D. implemented drivers, wrote actions, and conducted the experiments. F.R. implemented drivers pertaining to SDC and deployed machine learning algorithms to HELAO. J.F. integrated these contributions into the orchestrator. All authors reviewed the manuscript. Data Availability Statement: The data that support the findings of this study are openly available in [figshare] at [https://doi.org/10.6084/m9.figshare.16798177.v1], reference number . The authors declare no conflict of interest.
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