Although lithium-ion batteries(LIBs)currently dominate a wide spectrum of energy storage applications,they face challenges such as fast cycle life decay and poor stability that hinder their further application.To addr...Although lithium-ion batteries(LIBs)currently dominate a wide spectrum of energy storage applications,they face challenges such as fast cycle life decay and poor stability that hinder their further application.To address these limitations,element doping has emerged as a prevalent strategy to enhance the discharge capacity and extend the durability of Li-Ni-Co-Mn(LNCM)ternary compounds.This study utilized a machine learning-driven feature screening method to effectively pinpoint four key features crucially impacting the initial discharge capacity(IC)of Li-Ni-Co-Mn(LNCM)ternary cathode materials.These features were also proved highly predictive for the 50^(th)cycle discharge capacity(EC).Additionally,the application of SHAP value analysis yielded an in-depth understanding of the interplay between these features and discharge performance.This insight offers valuable direction for future advancements in the development of LNCM cathode materials,effectively promoting this field toward greater efficiency and sustainability.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
The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses(Ebuses).To optimize the deployment and operational strategies of Ebuses,it ...The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses(Ebuses).To optimize the deployment and operational strategies of Ebuses,it is imperative to accurately predict their energy consumption under varying conditions,particularly in cold climates where battery life is typically degraded.The exploration of this aspect within the Canadian context has been limited.In addition,we have found that existing models in the literature perform poorly in the Canadian environment,giving rise to the need for new models using Canadian data.This paper focuses on the development,comparison,and evaluation of various data-driven models designed to predict the energy consumption of different Ebuses with different heating technologies under a wide range of climate conditions.We specifically use Canadian data as a good representative of cold climates in general.The results show that the performance of the different bus types varies substantially under the exact same conditions.In addition,tree-based family of models proves to be the most suitable approach for predicting the Ebus consumption rate.The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate,with a resulting mean absolute error of 0.09–0.1 kWh/km observed across the different models.Furthermore,SHAP analysis shows that the main variables influencing the energy consumption rate depend on the type of heating system(using the battery for heating or using an auxiliary system that utilizes diesel for heating)adopted.展开更多
基金supported by the National Natural Science Foundation of China(Nos.52122408,52071023)the Program for Science&Technology Innovation Talents in the University of Henan Province(No.22HASTIT1006)+2 种基金the Program for Central Plains Talents(No.ZYYCYU202012172)the Ministry of Education,Singapore(No.RG70/20)the Opening Project of National Joint Engineering Research Center for Abrasion Control and Molding of Metal Materials,Henan University of Science and Technology(No.HKDNM201906).
文摘Although lithium-ion batteries(LIBs)currently dominate a wide spectrum of energy storage applications,they face challenges such as fast cycle life decay and poor stability that hinder their further application.To address these limitations,element doping has emerged as a prevalent strategy to enhance the discharge capacity and extend the durability of Li-Ni-Co-Mn(LNCM)ternary compounds.This study utilized a machine learning-driven feature screening method to effectively pinpoint four key features crucially impacting the initial discharge capacity(IC)of Li-Ni-Co-Mn(LNCM)ternary cathode materials.These features were also proved highly predictive for the 50^(th)cycle discharge capacity(EC).Additionally,the application of SHAP value analysis yielded an in-depth understanding of the interplay between these features and discharge performance.This insight offers valuable direction for future advancements in the development of LNCM cathode materials,effectively promoting this field toward greater efficiency and sustainability.
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.
文摘The global shift towards sustainable and environmentally friendly transportation options has led to the increasing adoption of electric buses(Ebuses).To optimize the deployment and operational strategies of Ebuses,it is imperative to accurately predict their energy consumption under varying conditions,particularly in cold climates where battery life is typically degraded.The exploration of this aspect within the Canadian context has been limited.In addition,we have found that existing models in the literature perform poorly in the Canadian environment,giving rise to the need for new models using Canadian data.This paper focuses on the development,comparison,and evaluation of various data-driven models designed to predict the energy consumption of different Ebuses with different heating technologies under a wide range of climate conditions.We specifically use Canadian data as a good representative of cold climates in general.The results show that the performance of the different bus types varies substantially under the exact same conditions.In addition,tree-based family of models proves to be the most suitable approach for predicting the Ebus consumption rate.The results indicate that the Random Forest method emerges as the superior choice for predicting the energy consumption rate,with a resulting mean absolute error of 0.09–0.1 kWh/km observed across the different models.Furthermore,SHAP analysis shows that the main variables influencing the energy consumption rate depend on the type of heating system(using the battery for heating or using an auxiliary system that utilizes diesel for heating)adopted.
