Two-dimensional (2D) materials have been developed into various catalysts with high performance, but employing them for developing highly stable and active nonprecious hydrogen evolution reaction (HER) catalysts still encounters many challenges. To this end, the machine learning (ML) screening of HER catalysts is accelerated by using genetic programming (GP) of symbolic transformers for various typical 2D MA2Z4 materials. The values of the Gibbs free energy of hydrogen adsorption (ΔG H*) are accurately and rapidly predicted via extreme gradient boosting regression by using only simple GP-processed elemental features, with a low predictive root-mean-square error of 0.14 eV. With the analysis of ML and density functional theory (DFT) methods, it is found that various electronic structural properties of metal atoms and the p-band center of surface atoms play a crucial role in regulating the HER performance. Based on these findings, NbSi2N4 and VSi2N4 are discovered to be active catalysts with thermodynamical and dynamical stability as ΔG H* approaches to zero (−0.041 and 0.024 eV). In addition, DFT calculations reveal that these catalysts also exhibit good deuterium evolution reaction (DER) performance. Overall, a multistep workflow is developed through ML models combined with DFT calculations for efficiently screening the potential HER and DER catalysts from 2D materials with the same crystal prototype, which is believed to have significant contribution to catalyst design and fabrication.

Recognition of volatile organic compounds (VOCs) is a hot topic full of challenge from the perspective of environmental protection and human security. Here, we developed a novel ratiometric cataluminescence (RCTL) method for fast identification and detection gas compounds at various concentrations based on the energy transfer process, by the means of introducing rare earth ions codoped metal oxide into cataluminescence (CTL) sensor system to work as sensing material. When the prepared stick-like Y2O3:Eu3+,Tb3+ is exposed to kinds of analytes, different energy transfer process take place to emit two new signals at the characteristic wavelength of Tb3+ (ITb) and Eu3+ (IEu), which is available for us to identify miscellaneous gaseous compounds rely on the ratio of ITb to IEu (ITb/IEu). To confirm the feasibility of the proposed method, seven kinds of gas compounds, including homologous series and even structural isomers, were investigated and successfully distinguished in a wide range of concentrations. Besides, further discussion of the CTL sensing and recognition mechanism in this paper has facilitating effects on exploring reactive intermediates and explaining the essential principle of catalytic oxidation process.

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We propose a new design for a cellular neural network with spintronic neurons and CMOS-based synapses. Harnessing the magnetoelectric and inverse Rashba-Edelstein effects allows natural emulation of the behavior of an ideal cellular network. This combination of effects offers an increase in speed and efficiency over other spintronic neural networks. A rigorous performance analysis via simulation is provided.INDEX TERMS Cellular neural network (CNN), CMOS, energy efficiency, magnetoelectric (ME), Rashba-Edelstein, spintronics.

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