Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks.The process usually includes structural optimization,energy calculation,charge analysis and ionic migratio...Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks.The process usually includes structural optimization,energy calculation,charge analysis and ionic migration performance estimation.The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures,and the computational cost is always high because of the task-intensive features.The last one relates to the kinetic problems,for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods.In this work,two predictive models,ionic migration activation energy model and structural optimization model,are developed based on machine learning(ML)techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures,respectively.By training 3136 energy barrier data calculated by bond valence(BV)method,an ionic migration activation energy model(Ea model)with mean absolute error(MAE)of 0.26 eV on testing data set is obtained.We apply this model and filter LiBiOS as a promising fast Li^(+)conductor from 49 Licontaining hetero-anionic compounds.Although the model-predicted result shows relatively low energy barrier,further analysis indicates that the high carrier formation energy restricts the ionic transportability.Therefore,we substitute fractional Li^(+)with Mg^(2+)in LiBiOS to relieve the large difficulty of forming carriers in the structure.In order to fast explore the optimal doping scheme,we develop the structural optimization model(E-f model)containing the ML-based energy and force prediction to accelerate the structural optimization under various LieMg ratio and doping configurations.Decent doping scheme Li_(1-2x)Mg_(x)BiOS(x=0.1875)shows much better Li^(+)migration performance compared with LiBiOS without substitution.This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations.展开更多
基金We acknowledge the National Natural Science Foundation of China(grant number 52022106)SAMSUNG Research China for financial support and idea explorationwell as Tianjin Supercomputer Center for providing computing resources.
文摘Fast ion conductor materials screening based on high-throughput calculations involves enormous computing tasks.The process usually includes structural optimization,energy calculation,charge analysis and ionic migration performance estimation.The first one involves looking for the equilibrium atomic positions in huge amount of candidate compounds or derivative structures,and the computational cost is always high because of the task-intensive features.The last one relates to the kinetic problems,for which the time-consuming transition state theory and the molecular dynamics are the main simulation methods.In this work,two predictive models,ionic migration activation energy model and structural optimization model,are developed based on machine learning(ML)techniques to accelerate the process of estimating activation energy and relaxing the doped crystal structures,respectively.By training 3136 energy barrier data calculated by bond valence(BV)method,an ionic migration activation energy model(Ea model)with mean absolute error(MAE)of 0.26 eV on testing data set is obtained.We apply this model and filter LiBiOS as a promising fast Li^(+)conductor from 49 Licontaining hetero-anionic compounds.Although the model-predicted result shows relatively low energy barrier,further analysis indicates that the high carrier formation energy restricts the ionic transportability.Therefore,we substitute fractional Li^(+)with Mg^(2+)in LiBiOS to relieve the large difficulty of forming carriers in the structure.In order to fast explore the optimal doping scheme,we develop the structural optimization model(E-f model)containing the ML-based energy and force prediction to accelerate the structural optimization under various LieMg ratio and doping configurations.Decent doping scheme Li_(1-2x)Mg_(x)BiOS(x=0.1875)shows much better Li^(+)migration performance compared with LiBiOS without substitution.This method of screening fast ion conductor materials and finding optimal doping scheme will extremely accelerate materials explorations.
