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

Science China Chemistry 2023年第11期66卷 3297-3306页

作者： Youcheng Hu Xiaoxiao Wang Peng Li Junxiang Chen Shengli Chen Hubei Electrochemical Power Sources Key Laboratory Department of ChemistryWuhan UniversityWuhan 430072China CAS Key Laboratory of Design and Assembly of Functional Nanostructures Fujian Institute of Research on the Structure of MatterChinese Academy of SciencesFuzhou 350002China

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 ***,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 *** 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 ***,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,***,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

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