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Novel earth‐abundant metal sulfate‐containing high entropy sulfides, FeNiCoCrXS2 (where X = Mn, Cu, Zn, or Al), are synthesized via a two‐step solvothermal method. It is shown that sulfate‐containing FeNiCoCrMnS2 exhibits superior oxygen evolution reaction (OER) activity with an exceptionally low overpotential of 199, 246, 285, and 308 mV at current densities of 10, 100, 500, and 1000 mA cm–2, respectively, and surpassing its unary‐, binary‐, ternary‐, and quaternary‐metal counterparts. The electrocatalyst yields exceptional stability after 12 000 cycles and 55 h of durability even at a high current density of 500 mA cm–2. Various in situ and ex situ analyses are used to investigate the self‐reconstruction of the sulfides during the OER for the first time. The resulting metal (oxy)hydroxide is believed to be the true active center for OER. The remaining sulfate also contributes to the catalytic activity. Density function theory calculation is in good agreement with the experimental result. The extraordinary OER performance of the high entropy sulfide brings a great opportunity for desirable catalyst design for practical applications. [ABSTRACT FROM AUTHOR]

High entropy oxides (HEOx) are novel materials, which increase the potential application in the fields of energy and catalysis. However, a series of HEOx is too novel to evaluate the synthesis properties, including formation and fundamental properties. Combining first-principles calculations with machine learning (ML) techniques, we predict the lattice constants and formation energies of spinel-structured photocatalytic HEOx, (Co,Cr,Fe,Mn,Ni)3O4, for stoichiometric and non-stoichiometric structures. The effects of site occupation by different metal cations in the spinel structure are obtained through first-principles calculations and ML predictions. Our predicted results show that the lattice constants of these spinel-structured oxides are composition-dependent and that the formation energies of those oxides containing Cr atoms are low. The computing time and computing energy can be greatly economized through the tandem approach of first-principles calculations and ML. [ABSTRACT FROM AUTHOR]