Normality of I-V Measurements Using ML

TL;DR

This paper presents a machine learning approach to validate the normality of I-V measurements in electrochemical experiments, ensuring data quality and detecting anomalies in automated electrochemistry workflows.

Contribution

The paper introduces a novel ML method for validating I-V measurements in electrochemical systems, integrating real-time system adaptation and anomaly detection.

Findings

ML method accurately detects abnormal I-V measurements

Automated workflow enables remote operation and data collection

Validation improves reliability of electrochemical data

Abstract

Electrochemistry ecosystems 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. 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. We demonstrate this automated workflow by remotely operating the instruments and collecting their measurements to generate a voltammogram (I-V profile) of an electrolyte solution in an electrochemical cell. These measurements are made available at the remote computing system and used for subsequent analysis. In this paper, we focus on a novel, analytically validated machine learning (ML) method for an electrochemistry ecosystem to ensure that I-V measurements are consistent with the normal experimental conditions, and to detect abnormal conditions, such as disconnected electrodes or low cell content volume.