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

TL;DR

This paper introduces an autonomous electrochemistry platform that integrates remote control, real-time data transfer, and machine learning for normality testing of voltammetry measurements, enhancing automation and reliability.

Contribution

It presents a novel integrated platform combining hardware, software, and ML techniques for autonomous electrochemical experiments with real-time abnormality detection.

Findings

Effective ML methods for abnormality detection demonstrated

Successful integration of mobile robot and synthesis workstation

Validation of ML model with theoretical generalization analysis

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.