![]() A novel intrinsic descriptor based on these five features is proposed, which can efficiently predict the performance of unknown catalysts. ![]() In descending order of importance, the five electronic and geometric features representing catalytic properties are group number, d-electron count, electronegativity, radius, and the number of nitrogen atoms. The low energy barrier results indicated that the best candidates were 4, 4, and 3C 1. Based on the few-shot learning algorithm, a machine learning model was built to reveal the underlying pattern between easily obtainable properties and energy barriers. The d-band theory was utilized to effectively describe the correlation between the adsorption energy of PMS and the electronic properties of catalysts. ![]() Three types of graphene-supported single-atom catalysts 2C 2, 3C 1, and 4, where M is a transition metal atom) were selected for PMS adsorption and activation. In this work, a series of transition metal-doped single-atom catalysts were used to investigate the intrinsic factors of PMS activation through combining density functional theory and machine learning. ![]() Persulfate-based advanced oxidation processes have been widely praised in the treatment of organic contaminants, but the intrinsic factors of peroxymonosulfate (PMS) activation have not been identified.
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