Bridging Deep Learning and In Situ Spectroelectrochemistry: A Hybrid Framework for Uncovering Fundamental Insights of Multi-Electron Processes

Unveiling thermodynamic and kinetic insights from rich, multidimensional spectroelectrochemical (SEC) datasets, particularly multi-electron transfer processes, remains largely reliant on multi-step chemometric and physics-based workflows, which are often sensitive to speciation complexity and experimental variability. Meanwhile, the application of machine learning to SEC is still untapped, in part due to the absence of standardized, physics-informed, ground-truth training datasets. In this work, tested on the two-electron reduction of anthraquinone in aprotic media, the implementation of a reference physics-based analytical methodology that combines chemometric techniques, evolving factor analysis, with derivative cyclic voltabsorptometry and the Butler–Volmer (BV) formalism is first elucidated. This reference methodology enabled the extraction of high-fidelity redox species concentrations, voltammetric currents, and estimation of mechanistic parameters and served as a validation benchmark for experimental SEC data. Building on this foundation, a deep learning (DL) framework capable of directly predicting concentrations, currents, and key thermodynamic and kinetic parameters (E0, k0) from raw SEC datasets is introduced. Central to this work, a novel hybrid approach was employed, leveraging the factorial and multivariate nature of SEC matrices, to integrate experimental fingerprints with systematically scalable in silico simulations from a BV-physics engine. Combining convolutional layers and a multi-task learning paradigm, the Feature-Fused Transfer Learning Model demonstrates strong generalization across both simulated and experimental SEC datasets under different scan rates. On unseen experimental data, the DL model accurately reconstructs concentration and current profiles and predicts electrochemical parameters with deviations below 0.01 V for E0 and 0.001 s–1 for k0. While classical theories and physics-based approaches remain the gold standards for highly rigorous mechanistic interpretation, the present DL framework offers a foundational step toward next-generation data-driven and automated SEC diagnostics.