A Deep Learning Approach for OER Electrocatalyst Kinetics Prediction

ABSTRACT Over the past decades, machine learning has been increasingly applied in electrochemistry, but challenges such as feature extraction and data scarcity persist. Here, we introduce a deep learning workflow that predicts Tafel slopes for the oxygen evolution reaction (OER) directly from electrochemical responses, overcoming data scarcity and enabling rapid evaluation of electrocatalyst performance. By capturing complex activity–structure relationships, this approach bypasses conventional parameter engineering, transfer learning, and DFT calculations, providing efficient data‐driven insights for sustainable water‐splitting electrocatalysts. This study proposes a novel application‐driven workflow that integrates conditional variational autoencoder (CVAE)‐based synthetic data generation with a one‐dimensional convolutional neural network (1D‐CNN) to predict Tafel slopes from OER electrocatalyst electrochemical measurements. CVAEs and CNNs are integrated deep learning architectures whose combined use augments sparse electrochemical datasets, validates synthetic data consistency, and enables accurate Tafel slope prediction in the context of OER kinetics for sustainable hydrogen production. The CVAE demonstrated capability in generating realistic synthetic data from only six real samples, with transfer tests confirming consistency between synthetic and real domains (TSTR: , RMSE = 2.736; TRTS: , RMSE = 5.474). Building on this, the 1D‐CNN provides predictions that align with the dataset, yielding an of 0.9990 (train) and 0.9986 (test) with low error values (MAE 1.18–1.45, RMSE 1.49–1.67). These findings highlights the promise of deep learning approach to surpass experimental data scarcity and allows a data‐driven rapid prediction of electrocatalysts kinetic for clean energy applications.