摘要
[EMIm]^(+)Cl^(-)+AlCl_(3)离子液体是一种在铝离子电池中具有突出应用前景的电解液.由于该离子液体中存在的可迁移离子种类多样(Al^(3+),AlCl_(3),[AlCl_4]^(-)和[Al_(2)Cl_(7)]^(-)),而且迁移离子类型之间存在实验研究尚未完全明晰的转换反应过程,这导致其离子迁移机制复杂且离子扩散动力学过程缓慢,难以通过常规的基于第一性原理分子动力学的方法实现具有显著统计意义的扩散动力学过程模拟.本文建立了不同浓度的[EMIm]^(+)Cl^(-)+AlCl_(3)离子液体原子尺度结构模型,并基于第一性原理分子动力学模拟和主动学习方法构建了训练集和测试集,实现了高精度的深度学习神经网络原子间势函数的拟合,其拟合能量和原子受力误差分别为5×10^(-4)e V/atom和5×10^(-2)eV/?.进一步通过比较深度学习势与第一性原理分子动力学模拟计算得到的[EMIm]^(+)Cl^(-)+AlCl_(3)离子液体径向分布函数和振动谱密度函数,佐证了机器学习势进行分子动力学计算的可靠性.最后,基于深度学习势函数的分子动力学开展了针对包含上万原子体系不同浓度比例的[EMIm]^(+)Cl^(-)+AlCl_(3)离子液体纳秒级别的扩散动力性质的研究,预测显示300 K下该系列离子液体中Al^(3+)扩散系数基本保持在4×10^(-7)cm^(2)/s.基于深度学习势分子动力学轨迹明确了Al^(3+)的两种主要扩散机制:其一为[AlCl_4]^(-)和[Al_(2)Cl_(7)]^(-)在不同溶剂壳层结构的迁移机制;其二为AlCl_(3)分子通过电解液中[Al_(2)Cl_(7)]^(-)与[AlCl_4]^(-)之间传递AlCl_(3)互相转换实现长程输运的过程.本文对Al离子电池离子液体电解液的Al^(3+)传输机理进行了更加深入的阐释,并进一步推动了机器学习势在模拟具有复杂分子结构和扩散动力学反应机制的电解液领域应用.
The[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquid is a promising prototype electrolyte for aluminum-ion batteries(AIBs).Its ionic transport behavior involves multiple mobile species(Al_(3)+,AlCl_(3),[AlCl_(4)]-and[Al_(2)Cl_(7)]^(-)),with ion migration mechanisms and conversion reactions among these species unsolved experimentally.This complexity results in heterogeneous ion migration mechanisms and sluggish diffusion kinetics,which cannot be accurately and reliably captured by the traditional first-principles molecular dynamics(FPMD)simulations within the very limited time duration(tens of ps)and relatively small modelling structure(less than 103 atoms).The classic molecular dynamics simulations based on various force fields are also scarce for studying and predicting the atomic structure evolution and ion diffusion dynamics of the complex electrolyte system such as ion liquids.In this work,a deep neural network interatomic potential(DP-potential)is developed through machine learning techniques,combining first-principles accuracy with classical molecular dynamics efficiency,to systematically investigate various chemical and physical properties for[EMIm]^(+)Cl^(-)+AlCl_(3)ion-liquid at finite temperatures.Training and validating of DP potential for[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquid are implemented with a two-stage protocol,including the primary training stage and the refining stage.Before initiating the two training stages,a series of first-principles molecular dynamics(FPMD)simulations is performed for[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquids with different molar ratios(1.0,1.3,1.5,1.7 and 2.0)and equilibrium densities(1.09—1.56 g/cm^(3))at finite temperatures(300 K and 400 K),resulting in a highly diverse training datasets spanning a board range of chemical compositions and densities during the primary training stage for DP potential.Then,the trained DPpotential is employed to conduct long-timescale classic molecular dynamics simulations by using LAMMPS program for the[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquids to produce the atomic configurations that either show significant errors in the calculated atomic forces and total energies or exhibit the unusual atomic evolution before crashing.Those highly extrapolated atomic configurations are merged with the initial training datasets to reoptimize the DP potential in the second refining stage.Through this two-stage training approach,a deep learning neural network interatomic potential with high accuracy is successfully constructed,achieving an energy prediction error of 5×10^(-4)eV/atom and a force prediction error of 5×10^(-2)eV/Å.The reliability of the finally obtained machine learning potential is further validated through a systematic comparison of radial distribution functions(RDF)for some representative atomic pairs such as C—N,C—H,Al—Cl and Cl—H,obtained from both DP-MD and FPMD,demonstrating excellent consistency for the results from the two methods.The DP-MD simulations are systematically carried out to investigate vibrational spectrum and Al_(3)+diffusion dynamics as well as possible conversion reactions among molecular or ionic species(Al_(3)+,AlCl_(3),[AlCl_(4)]-and[Al_(2)Cl_(7)]^(-))in[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquids within 10^(4)atoms at finite temperatures.From the calculated vibrational density of states(VDOS),it can be seen that the VDOS of[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquid can be approximated as a simple superposition of the vibrational spectra of individual species([EMIm]^(+),[AlCl_(4)]-,and[Al_(2)Cl_(7)]^(-)),with H related vibrational modes dominating above 90 THz and the Al—Cl modes dominating below 20 THz.At 300 K,DP-MD predicts that regardless of the chemical compositions,the diffusion coefficient of Al_(3)+remains around 4×10^(-7)cm^(2)/s at 300 K and the estimated diffusion activation energy is about 0.20 eV,which is very close to the experimental measurement value(0.15 eV).In addition,the calculated ionic conductivity of[EMIm]^(+)Cl^(-)+AlCl_(3)at room temperature is 27.37 mS/cm,with a deviation of only 18.2%from the experimental value(23.15 mS/cm).Notably,two different Al_(3)+diffusion mechanisms are identified in[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquid:1)direct migration processes conducted solely by molecular species including[AlCl_(4)]-and[Al_(2)Cl_(7)]^(-),and 2)the migration of the neutral AlCl_(3)molecule mediated with two neighboring[AlCl_(4)]-anions through the conversion reaction between[Al_(2)Cl_(7)]^(-)and AlCl_(3)+[AlCl_(4)]-moieties.Furthermore,first-principles calculations on the probable dissociation pathways of[Al_(2)Cl_(7)]^(-)revealed from DP-MD predict a reaction energy barrier height of 0.49 eV for the AlCl_(3)transferring between two[AlCl_(4)]-anions with an increased reaction probability from 0.00047 events/(ps·Al_(3)+)at 1∶1.3 molar ratio to 0.00347 events/(ps·Al_(3)+)at 1∶1.75 molar ratio.Overall,a highly efficient and reliable workflow to train and validate the deep neural network interatomic potential for complex electrolyte system is successfully proposed,such as[EMIm]^(+)Cl^(-)+AlCl_(3)ion liquids,thus providing a more comprehensive investigation of Al_(3)+transport mechanisms in ionic liquid electrolytes for aluminum-ion batteries.In conclusion,this work can further advance the application of machine learning-based potentials in simulating electrolyte systems characterized by complex molecular architectures and sluggish diffusion dynamics.
作者
刘浩良
何骅轩
曾超
吴锴
成永红
肖冰
LIU Haoliang;HE Huaxuan;ZENG Chao;WU Kai;CHENG Yonghong;XIAO Bing(State Key Laboratory of Electrical Insulation and Power Equipment,School of Electrical Engineering,Xi’an Jiaotong University,Xi’an 710049,China)
出处
《物理学报》
北大核心
2025年第19期355-369,共15页
Acta Physica Sinica
基金
国家自然科学基金(批准号:51807146)
中央高校基本科研业务费(批准号:xtr052024009)
西安交通大学青年拔尖人才计划(批准号:DQ1J009)
中央高校基本科研业务费-西安交通大学(批准号:xzy022023092)资助的课题。
关键词
离子液体
机器学习力场
分子动力学
铝离子电池
ion liquid
machine learning force field
molecular dynamics
aluminum-ion batteries
