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With the popularity of machine learning growing in the field of catalysis there are increasing numbers of catalyst databases becoming available. These databases provide us with the opportunity to search for catalysts with desired properties, which could lead to the discovery of new catalysts. However, while there are search methods for molecules based on similarity metrics, for solid-state catalyst systems there is not yet a straightforward search method. In this work, we propose a neural network embeddings based similarity search method that is applicable for both molecules and solid-state catalyst systems. We illustrate how the search method works and show search examples for the QM9, Materials Project (MP) and Open Catalyst 2020 (OC20) databases. We show that the configurations found present similarity in terms of geometry, composition, energy and in the electronic density of states. These results imply the neural network embeddings have encoded effective information that could be used to retrieve molecules and materials with similar properties. 

Doped graphitic single-atom catalysts (SACs) with isolated iron sites have similarities to natural enzymes and molecular biomimetics that can convert methane to methanol via a radical rebound mechanism with high-valent Fe( iv )O intermediates. To understand the relationship of SACs to these homogeneous analogues, we use range-separated hybrid density functional theory (DFT) to compare the energetics and structure of the direct metal-coordinating environment in the presence of 2p ( i.e. , N or O) and 3p ( i.e. , P or S) dopants and with increasing finite graphene model flake size to mimic differences in local rigidity. While metal–ligand bond lengths in SACs are significantly shorter than those in transition-metal complexes, they remain longer than SAC mimic macrocyclic complexes. In SACs or the macrocyclic complexes, this compressed metal–ligand environment induces metal distortion out of the plane, especially when reactive species are bound to iron. As a result of this modified metal-coordination environment, we observe SACs to simultaneously favor the formation of the metal–oxo while also allowing for methanol release. This reactivity is different from what has been observed for large sets of square planar model homogeneous catalysts. Overall, our calculations recommend broader consideration of dopants ( e.g. , P or S) and processing conditions that allow for local distortion around the metal site in graphitic SACs.