Computer Vision for Predicting Electrochemical Reaction Performance from Electroanalytical Data
Year: 2026
Type: preprint
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.
Source: ChemRxiv
Authors: Xinran Chen, Sven Erik Peters, Lutz Ackermann
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funder: Deutsche Forschungsgemeinschaft
Award: G1633987393
OpenAlex ID: W7214129201