Autonomous Electrochemistry Platform with Real-Time Normality Testing of Voltammetry Measurements Using ML

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Electrochemistry workflows utilize various instruments and computing systems to execute workflows consisting of electrocatalyst synthesis, testing and evaluation tasks. The heterogeneity of the software and hardware of these ecosystems makes it challenging to orchestrate a complete workflow from production to characterization by automating its tasks. We propose an autonomous electrochemistry computing platform for a multi-site ecosystem that provides the services for remote experiment steering, real-time measurement transfer, and AI/ML-driven analytics. We describe the integration of a mobile robot and synthesis workstation into the ecosystem by developing custom hub-networks and software modules to support remote operations over the ecosystem's wireless and wired networks. We describe a workflow task for generating I-V voltammetry measurements using a potentiostat, and a machine learning framework to ensure their normality by detecting abnormal conditions such as disconnected electrodes. We study a number of machine learning methods for the underlying detection problem, including smooth, non-smooth, structural and statistical methods, and their fusers. We present experimental results to illustrate the effectiveness of this platform, and also validate the proposed ML method by deriving its rigorous generalization equations.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE 20th International Conference on e-Science, e-Science 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350365610
DOIs
StatePublished - 2024
Event20th IEEE International Conference on e-Science, e-Science 2024 - Osaka, Japan
Duration: Sep 16 2024Sep 20 2024

Publication series

NameProceedings - 2024 IEEE 20th International Conference on e-Science, e-Science 2024

Conference

Conference20th IEEE International Conference on e-Science, e-Science 2024
Country/TerritoryJapan
CityOsaka
Period09/16/2409/20/24

Keywords

  • autonomous chemistry
  • cyclic voltammetry
  • electrochemical workflow
  • instrument-computing ecosystem
  • machine learning

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