论文标题
加速G-C $ _3 $ n $ _4 $ - 支持的单原子催化剂的发现，用于氢进化反应：合并的DFT和机器学习策略
Accelerating the Discovery of g-C$_3$N$_4$-Supported Single Atom Catalysts for Hydrogen Evolution Reaction: A Combined DFT and Machine Learning Strategy
论文作者
论文摘要
可以预见，由单个原子催化（SAC）支持的二维材料可以替代铂金以获得可持续氢产生的大规模工业可扩展性。在这里，一系列金属（Al，SC，Ti，V，Cr，Mn，Fe，Ni，Cu，Zn）和非金属（B，C，C，C，N，O，F，Si，Si，P，S，Cl）单原子嵌入了嵌入了各种活跃地点的G-C $ _3 $ N $ _4 $的各种活性地点，由DFT和六个机器学习（ML）筛选（Ml）Algorith（Ml）（Ml）（Ml）（Ml）（Ml）回归，随机森林回归，Adaboost回归，多层感知器回归，脊回归）。我们的结果基于地层能量，Gibbs自由能和带隙分析表明，B，MN和CO的单个原子锚定在G-C $ _3 $ N $ _4 $上可以用作高效的氢生产活性位点。基于支持矢量回归（SVR）的ML模型表现出最佳性能，可准确，迅速预测具有较低平均绝对误差（MAE）和高确定系数（R $^2 $）的氢吸附（$δ$ GH）的Gibbs自由能（$δ$ GH），分别为0.45和0.45和0.81。基于SVR模型的特征选择突出了前五名的主要特征：形成能量，键长，沸点，熔点和价电子作为关键描述。总体而言，通过DFT计算采用的多步骤工作流，结合了ML模型，以有效筛选从G-C $ _3 $ _4 $ _4 $ _4 $ _4 $ _4 $ _4 $ _4 $ _4 $ _4 $ _4的单原子催化作用可显着促进催化剂设计和制造。
Two-dimensional materials supported by single atom catalysis (SACs) are foreseen to replace platinum for large-scale industrial scalability of sustainable hydrogen generation. Here, a series of metal (Al, Sc, Ti, V, Cr, Mn, Fe, Ni, Cu, Zn) and non-metal (B, C, N, O, F, Si, P, S, Cl) single atoms embedded on various active sites of g-C$_3$N$_4$ are screened by DFT calculations and six machine learning (ML) algorithms (support vector regression, gradient boosting regression, random forest regression, AdaBoost regression, multilayer perceptron regression, ridge regression). Our results based on formation energy, Gibbs free energy and bandgap analysis demonstrate that the single atoms of B, Mn and Co anchored on g-C$_3$N$_4$ can serve as highly efficient active sites for hydrogen production. The ML model based on support vector regression (SVR) exhibits the best performance to accurately and rapidly predict the Gibbs free energy of hydrogen adsorption ($Δ$GH ) for the test set with a lower mean absolute error (MAE) and a high coefficient of determination (R$^2$) of 0.45 and 0.81, respectively. Feature selection based on the SVR model highlights the top five primary features: formation energy, bond length, boiling point, melting point, and valance electron as key descriptors. Overall, the multistep work-flow employed through DFT calculations combined with ML models for efficient screening of potential hydrogen evolution reaction (HER) from g-C$_3$N$_4$-based single atom catalysis can significantly contribute to the catalyst design and fabrication.
