Abstract
There is an increased interest in instrument-computing ecosystems (ICEs) that support science workflows empowered by AI-automated experiments and computations in diverse areas. In particular, electrochemistry ICEs are promising for accelerating the design and discovery of electrochemical systems for energy storage and conversion, by automating significant parts of workflows that combine synthesis and characterization experiments with computations. They require the integration of flow controllers, solvent containers, pumps, fraction collectors, and potentiostats, all connected to an electrochemical cell, as illustrated in Fig. 1. These are specialized instruments with custom software that is not originally designed for network integration. We developed network and software solutions for electrochemical workflows that adapt system and instrument settings in real-time for multiple rounds of experiments. In particular, we developed Python wrappers for Application Programming Interfaces (APIs) of instrument commands and Pyro client-server modules that enable them to be executed from remote computers. The entire workflow is orchestrated by a Jupyter notebook running on a remote computer.
| Original language | English |
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| Title of host publication | Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350322231 |
| DOIs | |
| State | Published - 2023 |
| Event | 19th IEEE International Conference on e-Science, e-Science 2023 - Limassol, Cyprus Duration: Oct 9 2023 → Oct 14 2023 |
Publication series
| Name | Proceedings 2023 IEEE 19th International Conference on e-Science, e-Science 2023 |
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Conference
| Conference | 19th IEEE International Conference on e-Science, e-Science 2023 |
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| Country/Territory | Cyprus |
| City | Limassol |
| Period | 10/9/23 → 10/14/23 |
Funding
This research is sponsored by the INTERSECT Initiative as part of the Laboratory Directed Research and Development Program and by RAMSES project of Advanced Scientific Computing Research program, U.S. Department of Energy (DOE), and in part by the Office of Basic Energy Sciences, Division of Materials Sciences and Engineering, U.S. Department of Energy, and is performed at Oak Ridge National Laboratory managed by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the DOE. The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes.