Computer Vision for Predicting Electrochemical Reaction Performance from Electroanalytical Data
- 1Georg‑August‑Universität Göttingen, Tammannstraße 2, Göttingen, Germany, 37077
- 2State Key Laboratory for Tea Plant Germplasm Innovation and Resource Utilization
- 3Anhui Agricultural University
Abstract
Electrosynthesis has surfaced as a key approach in modern synthesis, enabling more sustainable chemical transformations with reduced chemical waste. The accurate prediction of electrosynthesis performance is crucial for accelerating the discovery and design of reactions. Despite indisputable advances, the descriptor-based predictions fall short in representing the full reaction regime due to the high‑dimensional parameter space of electrosynthesis. Cyclic voltammograms, which are typically employed for mechanistic analysis, inherently contain rich information about electrochemical processes. Herein, we applied computer vision techniques to collect a cyclic voltammogram image database and trained a neural network directly on these image data. The resulting models achieved reliable performance in predicting electrosynthesis outcomes, demonstrating the potential of image-based deep learning for electrochemical reaction evaluation and data-driven electrosynthesis studies.
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BibTeX
@misc{chen2026computer,
title = {{Computer Vision for Predicting Electrochemical Reaction Performance from Electroanalytical Data}},
author = {Xinran Chen and Sven Erik Peters and Lutz Ackermann},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009343/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009343/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Computer Vision for Predicting Electrochemical Reaction Performance from Electroanalytical Data
AU - Xinran Chen
AU - Sven Erik Peters
AU - Lutz Ackermann
PY - 2026
DA - 2026/09/23
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009343/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009343/v1
AB - Electrosynthesis has surfaced as a key approach in modern synthesis, enabling more sustainable chemical transformations with reduced chemical waste. The accurate prediction of electrosynthesis performance is crucial for accelerating the discovery and design of reactions. Despite indisputable advances, the descriptor-based predictions fall short in representing the full reaction regime due to the high‑dimensional parameter space of electrosynthesis. Cyclic voltammograms, which are typically employed for mechanistic analysis, inherently contain rich information about electrochemical processes. Herein, we applied computer vision techniques to collect a cyclic voltammogram image database and trained a neural network directly on these image data. The resulting models achieved reliable performance in predicting electrosynthesis outcomes, demonstrating the potential of image-based deep learning for electrochemical reaction evaluation and data-driven electrosynthesis studies.
ER -