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

2024-09-16

IEEE International Conference on e-Science

AI Summary

To address the complexity, anomaly detection difficulty, and limited accessibility of electrochemical experiments in education, this paper proposes a multi-site collaborative automated electrochemical experimentation platform. We design a hardware architecture integrating mobile robots with synthetic workstations and develop a real-time voltammetric signal normality test method based on multi-paradigm machine learning—including smooth, nonsmooth, structured, and statistical models—to enable online detection of anomalies such as electrode disconnection. System integration is ensured via a custom Hub protocol, ROS-based control, and standardized electrochemical data stream interfaces. Theoretical analysis establishes convergence guarantees and generalization bounds for the proposed ML framework. Experimental evaluation demonstrates >98.7% anomaly detection accuracy and <0.5% false positive rate. The platform successfully supports remote, closed-loop cross-laboratory experiments, significantly lowering the barrier to electrochemical experimentation and facilitating its adoption in secondary science education.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Hardware-aware ML
• Natural Language Processing: Safety and Robustness

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Automated Electrochemical Platform
• Real-time Normality Check
• Simplified Experimental Process

Innovation

• Smart Electrochemical Platform
• Machine Learning
• Robotics Integration