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Oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and hydrogen evolution reaction (HER) are three critical reactions for energy-related applications, such as water electrolyzers and metal-air batteries. Graphene-supported single-atom catalysts (SACs) have been widely explored; however, either experiments or density functional theory (DFT) computations cannot screen catalysts at high speed. Herein, based on DFT computations of 104 graphene-supported SACs (M@C3, M@C4, M@pyridine-N4, and M@pyrrole-N4), we built up machine learning (ML) models to describe the underlying pattern of easily obtainable physical properties and limiting potentials (errors = 0.013/0.005/0.020 V for ORR/OER/HER, respectively), and employed these models to predict the catalysis performance of 260 other graphene-supported SACs containing metal-NxCy active sites (M@NxCy). We recomputed the top catalysts recommended by ML towards ORR/OER/HER by DFT, which confirmed the reliability of our ML model, and identified two OER catalysts (Ir@pyridine-N3C1 and Ir@pyridine-N2C2) outperforming noble metal oxides, RuO2 and IrO2. The ML models quantitatively unveiled the significance of various descriptors and fast narrowed down the potential list of graphene-supported single-atom catalysts. This approach can be easily used to screen and design other SACs, and significantly accelerate the catalyst design for many other important reactions. 

As emerging organic contaminants, siloxanes have severe impacts on the environment and human health. Simple linear siloxanes and derivates, trimethylsilanol (TMS), dimethylsilanediol (DMSD), monomethylsilanetriol (MMST), and dimethylsulfone (DMSO 2 ), are four persistent and common problematic compounds (PCs) from the hydroxylation and sulfuration of polydimethylsiloxanes. Herein, through a two-step computational process, namely Grand Canonical Monte Carlo (GCMC) simulations and machine learning (ML), we systematically screened 50 959 hypothetical pure-silica zeolites and identified 230 preeminent zeolites with excellent adsorption performances with all these four linear siloxanes and derivates. This work vividly demonstrates that the collocation of data-driven science and computational chemistry can greatly accelerate materials discovery and help solve the most challenging separation problems in environmental science.