Electrocatalytic nitrogen reduction to ammonia has garnered significant attention with the blooming of single-atom catalysts(SACs),showcasing their potential for sustainable and energy-efficient ammonia production.How...Electrocatalytic nitrogen reduction to ammonia has garnered significant attention with the blooming of single-atom catalysts(SACs),showcasing their potential for sustainable and energy-efficient ammonia production.However,cost-effectively designing and screening efficient electrocatalysts remains a challenge.In this study,we have successfully established interpretable machine learning(ML)models to evaluate the catalytic activity of SACs by directly and accurately predicting reaction Gibbs free energy.Our models were trained using non-density functional theory(DFT)calculated features from a dataset comprising 90 graphene-supported SACs.Our results underscore the superior prediction accuracy of the gradient boosting regression(GBR)model for bothΔg(N_(2)→NNH)andΔG(NH_(2)→NH_(3)),boasting coefficient of determination(R^(2))score of 0.972 and 0.984,along with root mean square error(RMSE)of 0.051 and 0.085 eV,respectively.Moreover,feature importance analysis elucidates that the high accuracy of GBR model stems from its adept capture of characteristics pertinent to the active center and coordination environment,unveilling the significance of elementary descriptors,with the colvalent radius playing a dominant role.Additionally,Shapley additive explanations(SHAP)analysis provides global and local interpretation of the working mechanism of the GBR model.Our analysis identifies that a pyrrole-type coordination(flag=0),d-orbitals with a moderate occupation(N_(d)=5),and a moderate difference in covalent radius(r_(TM-ave)near 140 pm)are conducive to achieving high activity.Furthermore,we extend the prediction of activity to more catalysts without additional DFT calculations,validating the reliability of our feature engineering,model training,and design strategy.These findings not only highlight new opportunity for accelerating catalyst design using non-DFT calculated features,but also shed light on the working mechanism of"black box"ML model.Moreover,the model provides valuable guidance for catalytic material design in multiple proton-electron coupling reactions,particularly in driving sustainable CO_(2),O_(2),and N_(2) conversion.展开更多
采用基于密度泛函(DFT)的非平衡态格林函数方法(non-equilibrium Green functions,NEGF),讨论电极距离对分子电子结构和电流的影响,计算四种脱氧核苷酸的I-V特性曲线和透射谱.结果表明,电极距离小于1.55nm时,电极距离对脱氧核苷酸分子...采用基于密度泛函(DFT)的非平衡态格林函数方法(non-equilibrium Green functions,NEGF),讨论电极距离对分子电子结构和电流的影响,计算四种脱氧核苷酸的I-V特性曲线和透射谱.结果表明,电极距离小于1.55nm时,电极距离对脱氧核苷酸分子的电子结构影响较大,当电极距离大于1.55nm时,LUMO和HOMO附近各能级的变化趋于平缓,电子能隙变化非常小.电极距离固定时,较低偏压(0.1～0.8V)不会改变四种脱氧核苷酸分子的相对电流大小.偏压较大时(0.8～1.0V),T和C的电流迅速增加并远大于A和G.展开更多
基金supported by the Research Grants Council of Hong Kong (City U 11305919 and 11308620)the NSFC/RGC Joint Research Scheme N_City U104/19The Hong Kong Research Grant Council Collaborative Research Fund:C1002-21G and C1017-22G。
文摘Electrocatalytic nitrogen reduction to ammonia has garnered significant attention with the blooming of single-atom catalysts(SACs),showcasing their potential for sustainable and energy-efficient ammonia production.However,cost-effectively designing and screening efficient electrocatalysts remains a challenge.In this study,we have successfully established interpretable machine learning(ML)models to evaluate the catalytic activity of SACs by directly and accurately predicting reaction Gibbs free energy.Our models were trained using non-density functional theory(DFT)calculated features from a dataset comprising 90 graphene-supported SACs.Our results underscore the superior prediction accuracy of the gradient boosting regression(GBR)model for bothΔg(N_(2)→NNH)andΔG(NH_(2)→NH_(3)),boasting coefficient of determination(R^(2))score of 0.972 and 0.984,along with root mean square error(RMSE)of 0.051 and 0.085 eV,respectively.Moreover,feature importance analysis elucidates that the high accuracy of GBR model stems from its adept capture of characteristics pertinent to the active center and coordination environment,unveilling the significance of elementary descriptors,with the colvalent radius playing a dominant role.Additionally,Shapley additive explanations(SHAP)analysis provides global and local interpretation of the working mechanism of the GBR model.Our analysis identifies that a pyrrole-type coordination(flag=0),d-orbitals with a moderate occupation(N_(d)=5),and a moderate difference in covalent radius(r_(TM-ave)near 140 pm)are conducive to achieving high activity.Furthermore,we extend the prediction of activity to more catalysts without additional DFT calculations,validating the reliability of our feature engineering,model training,and design strategy.These findings not only highlight new opportunity for accelerating catalyst design using non-DFT calculated features,but also shed light on the working mechanism of"black box"ML model.Moreover,the model provides valuable guidance for catalytic material design in multiple proton-electron coupling reactions,particularly in driving sustainable CO_(2),O_(2),and N_(2) conversion.
文摘采用基于密度泛函(DFT)的非平衡态格林函数方法(non-equilibrium Green functions,NEGF),讨论电极距离对分子电子结构和电流的影响,计算四种脱氧核苷酸的I-V特性曲线和透射谱.结果表明,电极距离小于1.55nm时,电极距离对脱氧核苷酸分子的电子结构影响较大,当电极距离大于1.55nm时,LUMO和HOMO附近各能级的变化趋于平缓,电子能隙变化非常小.电极距离固定时,较低偏压(0.1～0.8V)不会改变四种脱氧核苷酸分子的相对电流大小.偏压较大时(0.8～1.0V),T和C的电流迅速增加并远大于A和G.
