Investigating Catalytic Properties of Transition-Metal Compounds Using Tailored DFT Methods

The strongly constrained and appropriately normed (r2SCAN) meta-generalized gradient approximation (meta-GGA DFT Method) offers an efficient and accurate approach to studying the catalytic properties of transition metal compounds for the oxygen evolution reaction (OER). This is particularly true when we add the Hubbard correction, +U, which provides an inexpensive

self-interaction correction. Manganese oxides, particularly layered MnO2, are promising catalysts due to mixed Mn(III) and Mn(IV) oxidation states, which enhance catalytic performance by lowering the OER overpotential. Recent research shows that manipulating the oxidation state layering can optimize electronic structures for improved catalytic efficiency.

Using r2SCAN+U, we computationally investigate how varying oxidation state configurations in MnO2, NiO2, and KCoO2 affect OER activity. Additionally, we analyze how layered structures in CoFe and NiFe hydroxides contribute to reduced overpotentials. Our results aim to uncover how oxidation state tuning and structural modifications influence catalytic efficiency. This work advances DFT's predictive capabilities and aids the design of high-performance, stable catalysts for clean hydrogen production and renewable energy applications.