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Methane clathrates are widespread on the ocean floor of the Earth. A better understanding of methane clathrate formation has important implications for natural-gas exploitation, storage, and transportation. A key step toward understanding clathrate formation is hydrate nucleation, which has been suggested to involve multiple evolution pathways. Herein, a unique nucleation/growth pathway for methane clathrate formation has been identified by analyzing the trajectories of large-scale molecular dynamics (MD) simulations. In particular, ternary water-ring aggregations (TWRAs) have been identified as fundamental structures for characterizing the nucleation pathway. Based on this nucleation pathway, the critical nucleus size and nucleation timescale can be quantitatively determined. Specifically, a methane hydration layer compression/shedding process is observed to be the critical step in (and driving) the nucleation/growth pathway, which is manifested through overlapping/compression of the surrounding hydration layers of the methane molecules, followed by detachment (shedding) of the hydration layer. As such, an effective way to control methane hydrate nucleation is to alter the hydration layer compression/shedding process during the course of nucleation.

 

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.