Understanding the sluggish and highly variable transport kinetics of lithium ions in LiFePO_4  

作　　者：Youcheng Hu Xiaoxiao Wang Peng Li Junxiang Chen Shengli Chen 

机构地区：[1]Hubei Electrochemical Power Sources Key Laboratory,Department of Chemistry,Wuhan University,Wuhan 430072,China [2]CAS Key Laboratory of Design and Assembly of Functional Nanostructures,Fujian Institute of Research on the Structure of Matter,Chinese Academy of Sciences,Fuzhou 350002,China

出　　处：《Science China Chemistry》2023年第11期3297-3306,共10页中国科学（化学英文版）

基　　金：financially supported by the National Natural Science Foundation of China(22272122,21832004 and 21673163)。

摘　　要：LiFePO_(4),one of the mainstream cathode materials of current EV batteries,exhibits experimental diffusion coefficients(D_(c))of Li^(+)which are not only several orders of magnitude lower than those predicted by the ionic hopping barriers obtained from theoretical calculations and spectroscopic measurements,but also span several orders from 10^(-14)to 10^(-18)cm^(2)s^(-1)under different states of charge(SOC)and the charging rates(C-rates).Atomic level understanding of such sluggishness and diversity of Li^(+)transport kinetics would be of significance in improving the rate performance of LiFePO_(4)through material and operation optimization but remain challenging.Herein,we show that the high sensitivity of Li^(+)hopping barriers on the local Li–Li coordination environments(numbers and configurations)plays a key role in the ion transport kinetics.This is due a neural network-based deep potential(DP)which allows accurate and efficient calculation of hopping barriers of Li^(+)in LiFePO_(4)with various Li–Li coordination environments,with which the kinetic Monte-Carlo(KMC)method was employed to determine the D_(c)values at various C-rates and SOC across a broad spectrum.Especially,an accelerated KMC simulation strategy is proposed to obtain the D_(c)values under a wide range of SOC at low C-rates,which agree well with that obtained from the galvanostatic intermittent titration technique(GITT).The present study provides accurate descriptions of Li^(+)transport kinetics at both very high and low C-rates,which remains challenging to experiments and first-principles calculations,respectively.Finally,it is revealed that the gradient distributions of Li^(+)density along the diffusion path result in great asymmetry in the barriers of the forward and backward hopping,causing very slow diffusion of Li^(+)and the diverse variation of D_(c).

关 键 词：lithium iron phosphate diffusion coefficient machine-learning potential kinetic Monte Carlo simulations 

分 类 号：TQ131.11[化学工程—无机化工] TM912[电气工程—电力电子与电力传动]