Machine Learning-Assisted Molecular Orbital Insights into OER Activity Descriptors of Component Gradient Ni-Based LDH Electrocatalysts

Abstract

The conventional theories to predict the oxygen evolution reaction (OER) performance in electrochemical water-electrolysis, including the d-band center and the e_g orbital occupancy, encounter limitations under specific conditions. The d-band center serves as a partial descriptor of adsorption energy, leading to inconsistencies, and the e_g orbital occupancy theory underestimates the contributions of other orbitals. Here, a machine learning-assisted molecular orbital investigation is conducted to explore 3d orbitals characteristics. To account for the crystal field effect and mitigate partition errors arising from orbital degeneracy, 3d orbitals are categorized into e_g and t_2g. The proposed descriptors are designed not only to predict performance but also to aid in elucidating the underlying determinants of performance. It elucidates nuanced performance determinants that are context-dependent and can be categorized into two distinct types: electron-deficient, e.g., Fe (3d⁶) and Co (3d⁷), and electron-rich, e.g., Cu (3d⁹) and Zn (3d¹⁰). For electron-deficient metals, the orbitals are unoccupied, with the electrons populating the t_2g orbital preferentially released as the valence state increases, thereby influencing performance, and vice versa. In summary, this work establishes a complex correlation between molecular orbitals and catalytic activity via ML, offering a novel perspective for advancing the design and elucidating the mechanisms of high-performance OER electrocatalysts.