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Two-dimensional (2D) materials, such as transition metal dichalcogenides (TMDCs) in the 2H or 1T crystal phases, are promising (electro)catalyst candidates due to their high surface to volume ratio and composition of low-cost, abundant elements. While the edges of elemental TMDC nanoparticles, such as MoS$_2$, can show significant catalytic activity, the basal plane of the pristine materials are notoriously inert, which limits their normalized activity. Here we show that high densities of catalytically active sites can be formed on the TMDC basal plane by alloying elements that prefer the 2H (1T) phase into a 1T (2H) structure. The global stability of the alloy, in particular whether it crystallizes in the 2H or 1T phase, can be controlled by ensuring a majority of elements preferring the target phase. We further show that the mixing entropy plays a decisive role for stabilizing the alloy implying that high-entropy alloying becomes essential. Our calculations point to a number of interesting non-precious hydrogen evolution catalysts, including (CrHfTaVZr)S$_2$ and (CrNbTiVZr)S$_2$ in the T-phase and (MoNbTaVTi)S$_2$ in the H-phase. Our work opens new directions for designing catalytic sites via high-entropy alloy stabilization of locally unstable structures.

The optimization of atomic structures plays a pivotal role in understanding and designing materials with desired properties. However, conventional computational methods often struggle with the formidable task of navigating the vast potential energy surface, especially in high-dimensional spaces with numerous local minima. Recent advancements in machine learning-driven surrogate models offer a promising avenue for alleviating this computational burden. In this study, we propose a novel approach that combines the strengths of universal machine learning potentials with a Bayesian approach using Gaussian processes. By using the machine learning potentials as priors for the Gaussian process, the Gaussian process has to learn only the difference between the machine learning potential and the target energy surface calculated for example by density functional theory. This turns out to improve the speed by which the global optimal structure is identified across diverse systems for a well-behaved machine learning potential. The approach is tested on periodic bulk materials, surface structures, and a cluster.