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567 results on '"Jin, Guangyong"'

36. Designing a flower-shaped ZnS/CoS heterojunction for efficient electroreduction of N2 to NH3.

Author

Gao, Ze, Xu, Ming, Liu, Renming, Xu, Hang, Chu, Dongxue, Yang, Daming, Feng, Ming, Wang, Ting, and Jin, Guangyong

Subjects
NITROGEN, AMMONIA, CATALYSTS, DESIGN
Abstract

The electrocatalytic nitrogen reduction reaction (NRR) is a highly promising process for synthesizing ammonia and holds great potential to replace the traditional Haber–Bosch process. Here, we report a novel flower-shaped ZnS/CoS composite electrocatalyst for the NRR. Remarkably, the ZnS/CoS-105 heterojunction catalyst achieved an NH3 yield rate of 20.42 μg h−1 mgcat.−1 and a faradaic efficiency (FE) of 11.83% at −0.45 V VS. RHE in an aqueous 0.1 M Na2SO4 solution. In addition, ZnS/CoS-105 showed remarkable stability (up to 24 h) for the NRR process. [ABSTRACT FROM AUTHOR]

Published
2024
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37. The Application of Supervised Learning Algorithms in Predicting the Formation Energy of NLO Crystals.

Author

Wang, Ai, Yin, Yaohui, Sun, Zhixin, Jin, Guangyong, and Xin, Chao

Subjects
MACHINE learning, FREQUENCY changers, STANDARD deviations, SUPERVISED learning, DENSITY functional theory
Abstract

Nonlinear optical crystals (NLO) are a key class of functional materials in the field of laser technology due to their excellent frequency conversion effects and physical–chemical stability. The research aims to find NLO crystals with superior stability by predicting their formation energy. In this study, only compositional information is utilized as input features and models are constructed using regression algorithms such as Random Forest Regression (RFR), Support Vector Regression (SVR), and Gradient Boosting Regression (GBR). Notably, the GBR model exhibited outstanding predictive performance, with an R2 value of 0.935 and root mean square error (RMSE) of 0.248 eV per atom. Additionally, SHapley Additive exPlanations (SHAP) analysis is employed to elucidate the fundamental principles behind the predictions by assessing the contribution of each feature to the formation energy. To validate the reliability of the models, first‐principles calculations are conducted to predict the formation energy of materials of GaP, ZnGeP2, and CdSiP2. The error range between the model predictions and the Generalized Gradient Approximation (GGA) calculated values is ≈0.1 eV per atom, confirming the accuracy of the models. [ABSTRACT FROM AUTHOR]

Published
2024
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44. Predictive nonlinear optical crystal formation energy regression model based on convolutional neural networks.

Author

Fan, Zhen, Lian, Shuai, Jin, Guangyong, Xin, Chao, Li, Ye, and Yuan, Boshi

Subjects
CONVOLUTIONAL neural networks, REGRESSION analysis, NUCLEAR charge, CRYSTALS, DEEP learning
Abstract

In modern laser science, nonlinear optical crystals play a crucial role as indispensable functional materials. Overcoming the inherent defects of traditional materials such as two-photon absorption and low laser damage thresholds, the search for high-performance nonlinear optical crystals remains an urgent and unresolved challenge. In this study, we extensively utilized deep learning techniques to construct a lightweight convolutional neural network. This network is built on the basis of information about the nuclear charge of elements and the number of atoms in compounds, creating carefully designed feature maps for accurately predicting the formation energy of nonlinear optical crystals. Our model demonstrates outstanding performance, with predictive metrics showing R2 = 0.985, RMSE = 0.128, and MAE = 0.083. This remarkable predictive capability enables our network to efficiently predict the formation energy of crystals and reliably assess their stability. Our approach is poised to play a crucial role in high-throughput screening, significantly reducing computational burdens and accelerating the discovery process of novel crystals. This significant achievement provides an efficient and reliable tool for crystal material research, opening up new possibilities for future materials science investigations. [ABSTRACT FROM AUTHOR]

Published
2024
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