Overview
This project presents a comprehensive suite of deep learning methods for electrochemical data analysis.
Our work spans four major contributions to the field, addressing critical challenges in cyclic voltammetry, electrochemical impedance spectroscopy, and electrochemical automation.
and electrochemical impedance spectroscopy analysis through advanced machine learning techniques.
We implement state-of-the-art neural network architectures, including Faster R-CNN with custom 1D ResNet backbones,
specifically designed for time-series electrochemical data. Our models can automatically detect and classify
multiple redox events, discern electrochemical mechanisms, and provide optimal data preprocessing strategies
for machine learning applications in electrochemistry.
🔍 Automated Detection
Simultaneously detect and classify multiple redox events in complex cyclic voltammograms with high accuracy.
🎯 Mechanism Identification
Automatically discern electrochemical mechanisms (E, EC, ECE, DISP, etc.) from experimental data.
📊 Robust Analysis
Handle noisy data, various scan rates, and challenging experimental conditions effectively.
🛠️ EIS Preprocessing
Comprehensive guidelines for optimal preprocessing of electrochemical impedance spectroscopy data.
Publications
Redox-Detecting Deep Learning for Mechanism Discernment in Cyclic Voltammograms of Multiple Redox Events
Benjamin B. Hoar, et al.
ACS Electrochemistry, Volume 1, Issue 1, Pages 52-62 (2024)
Abstract: In electrochemical analysis, mechanism assignment is fundamental to understanding the chemistry of a system. The detection and classification of electrochemical mechanisms in cyclic voltammetry set the foundation for subsequent quantitative evaluation and practical application, but are often based on relatively subjective visual analyses. Deep-learning (DL) techniques provide an alternative, automated means that can support experimentalists in mechanism assignment. Herein, we present a custom DL architecture dubbed as EchemNet, capable of assigning both voltage windows and mechanism classes to electrochemical events within cyclic voltammograms of multiple redox events. The developed technique detects over 96% of all electrochemical events in simulated test data and shows a classification accuracy of up to 97.2% on redox events with 8 known mechanisms. This newly developed DL model, the first of its kind, proves the feasibility of redox-event detection and electrochemical mechanism classification with minimal a priori knowledge. The DL model will augment human researchers’ productivity and constitute a critical component in a general-purpose autonomous electrochemistry laboratory.
Electrochemical Mechanistic Analysis from Cyclic Voltammograms Based on Deep Learning
Benjamin B. Hoar, et al.
ACS Measurement Science Au, Volume 2, Issue 6, Pages 595-604 (2022)
Abstract: For decades, employing cyclic voltammetry for mechanistic investigation has demanded manual inspection of voltammograms. Here, we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a probable electrochemical mechanism among five of the most common ones in homogeneous molecular electrochemistry. The reported algorithm will aid researchers’ mechanistic analyses, utilize otherwise elusive features in voltammograms, and experimentally observe the gradual mechanism transitions encountered in electrochemistry. An automated voltammogram analysis will aid the analysis of complex electrochemical systems and promise autonomous high-throughput research in electrochemistry with minimal human interference.
Inquiry into the Appropriate Data Preprocessing of Electrochemical Impedance Spectroscopy for Machine Learning
Jingwen Sun, et al.
The Journal of Physical Chemistry C, Volume 129, Issue 2, Pages 1044-1051 (2025)
Abstract: Electrochemical impedance spectroscopy (EIS) is an important analytical technique for the understanding of electrochemical systems. With the recent advent and burgeoning deployment of machine learning (ML) in EIS analysis, a critical yet hitherto unanswered question emerges: what is the appropriate manner to preprocess the EIS data for ML-based analysis? While the preprocessing of a model's input data is known to be critical for a successful deployment of the ML model, EIS is known to possess multiple classical venues of data representation, and moreover, a proper data normalization protocol for comparative EIS studies remains elusive. Here, we report the methodology and the outcomes that evaluate the efficacy of multiple data preprocessing methods in an ML-based EIS analysis. Within our proof-of-concept parameter space, plotting the input training data's impedance magnitude against phase angle while individually normalizing each EIS curve yields the highest accuracy and robustness in the correspondingly established residual neural network (ResNet) model. Rationalized by additional “importance” analysis of the input data, such a data representation method extracts information and hidden features more effectively. While the Nyquist plot is widely used in manual analysis, a different data representation of EIS data seems equally plausible for ML-based EIS analysis. Our work offers a protocol for future researchers to decide on the proper preprocessing method for different ML applications in electrochemistry on a case-by-case basis.
Autonomous closed-loop mechanistic investigation of molecular electrochemistry via automation
Hongyuan Sheng, et al.
Nature Communications, Volume 15, Issue 1, Pages 2781 (2024)
Abstract: Electrochemical research often requires stringent combinations of experimental parameters that are demanding to manually locate. Recent advances in automated instrumentation and machine-learning algorithms unlock the possibility for accelerated studies of electrochemical fundamentals via high-throughput, online decision-making. Here we report an autonomous electrochemical platform that implements an adaptive, closed-loop workflow for mechanistic investigation of molecular electrochemistry. As a proof-of-concept, this platform autonomously identifies and investigates an EC mechanism, an interfacial electron transfer (E step) followed by a solution reaction (C step), for cobalt tetraphenylporphyrin exposed to a library of organohalide electrophiles. The generally applicable workflow accurately discerns the EC mechanism’s presence amid negative controls and outliers, adaptively designs desired experimental conditions, and quantitatively extracts kinetic information of the C step spanning over 7 orders of magnitude, from which mechanistic insights into oxidative addition pathways are gained. This work opens opportunities for autonomous mechanistic discoveries in self-driving electrochemistry laboratories without manual intervention.
Installation & Usage
Quick Start
Clone the repository and set up the environment:
git clone https://github.com/UNCSciML/redox-detecting.git
cd redox-detecting
conda env create -f environment.yml
conda activate chem
Training
python train.py
Testing
python test.py
For detailed testing instructions and experiment configurations, please refer to README_TEST.md.
Configuration
Model parameters and training configurations can be adjusted in src/config/parameters.py.
This includes settings for data paths, noise levels, scan rate configurations, and model hyperparameters.