The commercialization of perovskite solar cells(PSCs)is hindered by the instability of organic components and the resource-intensive nature of experimental optimization.Machine learning(ML)is revolutionizing the disco...The commercialization of perovskite solar cells(PSCs)is hindered by the instability of organic components and the resource-intensive nature of experimental optimization.Machine learning(ML)is revolutionizing the discovery and optimization of photovoltaic devices by reducing reliance on conventional trial-and-error approaches.This study aims to optimize the performance of CsPbI₃-based all-inorganic PSCs using a combined SCAPS-1D and machine learning(ML)approach.We generated 56,390 unique device configurations via SCAPS-1D simulations,varying layer thicknesses and defect densities.Five ML models were trained,with XGBoost achieving the highest accuracy(R^(2)=0.999).Feature importance was analyzed using SHAP.Optimization increased the PCE from 15.15%to 19.16%,with the perovskite layer thickness(2μm)and defect density(<10^(15)cm^(-3))identified as critical parameters.This study highlights the potential of ML-driven optimization in perovskite solar cells,offering a systematic and data-driven approach to enhancing device efficiency and accelerating the development of next-generation photovoltaics.展开更多
This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively dist...This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively distinguishing qualified and unqualified catalysts.The prediction accuracy rate is high,up to 95%.The SHapley Additive exPlanations(SHAP)analysis reveals that the N≡N bond length and the number of outermost d electrons(N_(d))can well describe the nitrogen(N2)reduction reaction(NRR)activity.The relationships between N≡N,N_(d),the adsorption energies of different intermediates(ΔE_(*N_(2)),ΔE_(*N_(2)H),and ΔE_(*NH_(2))),the general descriptor(φ),and the Gibbs free energy of key steps(ΔG_(*N_(2)),ΔG_(*N_(2)-*N_(2)H),and ΔG_(*N_H(2)-*NH_(3)))indicate that moderate nitrogen activation can enhance the reaction activity.Among the 17 screened SACs,Mo@S3N1,and W@S_(3)N_(1) demonstrate the best catalytic performance,with limiting potential(U_(L))values of only-0.26 and-0.25 V under implicit solvation conditions.The electronic properties and variations in N≡N and TM-N bond lengths are investigated to reveal the origin of NRR activity.This study provides the decisive features and NRR dataset for ML research,as well as a feasible strategy for rational design of NRR SACs.展开更多
基金supported by the EU Horizon2020 Project Marketplace,No.760173.
文摘The commercialization of perovskite solar cells(PSCs)is hindered by the instability of organic components and the resource-intensive nature of experimental optimization.Machine learning(ML)is revolutionizing the discovery and optimization of photovoltaic devices by reducing reliance on conventional trial-and-error approaches.This study aims to optimize the performance of CsPbI₃-based all-inorganic PSCs using a combined SCAPS-1D and machine learning(ML)approach.We generated 56,390 unique device configurations via SCAPS-1D simulations,varying layer thicknesses and defect densities.Five ML models were trained,with XGBoost achieving the highest accuracy(R^(2)=0.999).Feature importance was analyzed using SHAP.Optimization increased the PCE from 15.15%to 19.16%,with the perovskite layer thickness(2μm)and defect density(<10^(15)cm^(-3))identified as critical parameters.This study highlights the potential of ML-driven optimization in perovskite solar cells,offering a systematic and data-driven approach to enhancing device efficiency and accelerating the development of next-generation photovoltaics.
基金supported by National Natural Science Foundation of China(Nos.52271136 and 22373063)the Natural Science Foundation of Shaanxi Province in China(Nos.2021JC-06 and 2019TD-020)Fundamental Research Funds for the Central Universities of China(No.GK202203002).
文摘This study constructs 196 transition metals(TM)@S_(x)N_(y) single-atom catalysts(SACs)(x=0-4 and y=0-4)and employs the eXtreme Gradient Boosting(XGBoost)classification model in machine learning(ML)for effectively distinguishing qualified and unqualified catalysts.The prediction accuracy rate is high,up to 95%.The SHapley Additive exPlanations(SHAP)analysis reveals that the N≡N bond length and the number of outermost d electrons(N_(d))can well describe the nitrogen(N2)reduction reaction(NRR)activity.The relationships between N≡N,N_(d),the adsorption energies of different intermediates(ΔE_(*N_(2)),ΔE_(*N_(2)H),and ΔE_(*NH_(2))),the general descriptor(φ),and the Gibbs free energy of key steps(ΔG_(*N_(2)),ΔG_(*N_(2)-*N_(2)H),and ΔG_(*N_H(2)-*NH_(3)))indicate that moderate nitrogen activation can enhance the reaction activity.Among the 17 screened SACs,Mo@S3N1,and W@S_(3)N_(1) demonstrate the best catalytic performance,with limiting potential(U_(L))values of only-0.26 and-0.25 V under implicit solvation conditions.The electronic properties and variations in N≡N and TM-N bond lengths are investigated to reveal the origin of NRR activity.This study provides the decisive features and NRR dataset for ML research,as well as a feasible strategy for rational design of NRR SACs.
