In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and attention.Solid-state electrolytes,as the ke...In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and attention.Solid-state electrolytes,as the key component of next-generation battery technology,are favored for their high safety,high energy density,and long life.However,finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications.Focusing on inorganic solid-state electrolytes,this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity,good thermal stability,and structural and phase stability.Traditional experimental and theoretical computational methods suffer from inefficiency,thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics.Through the gradient descent-based XGBoost algorithm,we successfully predicted the energy band structure and stability of the materials,and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously,which greatly accelerated the development of solid-state batteries.展开更多
基金supported by the National Natural Science Foundation of China(No.21421063,No.21473166,No.21573211,No.21633007,No.21790350,No.21803067,No.91950207)the Chinese Academy of Sciences(QYZDB-SSW-SLH018)+3 种基金the Anhui Initiative in Quantum Information Technologies(AHY090200)the USTC-NSRL Joint Funds(UN2018LHJJ)the Anhui Provincial Natural Science Foundation(2108085QB63)Numerical Theoretical simulations were done in the Supercomputing Center of USTC.
文摘In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and attention.Solid-state electrolytes,as the key component of next-generation battery technology,are favored for their high safety,high energy density,and long life.However,finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications.Focusing on inorganic solid-state electrolytes,this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity,good thermal stability,and structural and phase stability.Traditional experimental and theoretical computational methods suffer from inefficiency,thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics.Through the gradient descent-based XGBoost algorithm,we successfully predicted the energy band structure and stability of the materials,and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously,which greatly accelerated the development of solid-state batteries.
