Machine Learning-Guided Design of High-Entropy FeCoCrMnCu Layered Double Hydroxides for Efficient Oxygen Evolution in Alkaline Media
Chandrasekaran Pitchai, Chao-Fang Huang, Ting-Yu Lo, Hui Li, Ming-Der Yang, Chih-Ming Chen
Abstract
High Resolution Image Download MS PowerPoint Slide The discovery of high-performance oxygen evolution reaction (OER) catalysts is often hindered by the vast compositional space of high-entropy materials, making conventional trial-and-error methods time-consuming and resource-intensive. In this work, we demonstrate a machine learning (ML)-guided strategy for the design of high-entropy FeCoCrMnCu layered double hydroxides (LDHs) as advanced OER catalysts in alkaline media. An experimental data set of only 70 compositions was used to train an Extreme Gradient Boosting (XGBoost) regression model, which achieved high predictive accuracy ( R 2 = 0.84, RMSE = 9.95 mV). The ML model identified an optimal composition (Fe 0 . 15 Co 0 . 10 Cr 0 . 30 Mn 0 . 30 Cu 0 . 15 ) with a predicted overpotential of 261.3 mV, closely matching the experimentally obtained 270 mV (error ∼ 3%). This approach effectively reduced the need for exhaustive testing of more than 10,626 possible compositions, achieving a 99.3% reduction in time and effort. The ML-optimized catalyst exhibited favorable morphology, homogeneous elemental distribution, and strong intrinsic activity, with a Tafel slope of 74.2 mV dec –1, high turnover frequency (0.225 s –1 ), and stable operation for 72 h. This study highlights the power of integrating ML with entropy-driven materials design to accelerate the development of next-generation electrocatalysts.