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Volodymyr Koverga

Publications and source records attributed to Volodymyr Koverga.

3 recordsLinked to original sources

Unveiling the Lithium-Ion Transport Mechanism in Li2ZrCl6 Solid-State Electrolyte via Deep Learning-Accelerated Molecular Dynamics Simulations

Lithium zirconium chlorides (LZCs) present a promising class of cost-effective solid electrolyte for next-generation all-solid-state batteries. The unique crystal structure of LZCs plays a crucial role in facilitating lithium-ion mobility, which further affects its electrochemical performance. To understand the underlying mechanism governing ion transport, we employed deep learning-accelerated molecular dynamics simulation on Li2ZrCl6 (trigonal α- and monoclinic \b{eta}-LZC), focusing specifically on the zirconium coordination environment. Our results reveal that disordered α-LZC exhibits the highest ionic conductivity, while \b{eta}-LZC demonstrates significantly lower conductivity, closely aligning with experimental findings. The study confirms that across all phases, lithium migration proceeds via site-to-site hopping mechanism, where variations in site residence times critically impact the overall ionic conductivity. In α-LZCs, lithium ions prefer to anisotropically diffuse across interlayers as the result of lower energy barrier, driven primarily by collective diffusion. In contrast, lithium ions in \b{eta}-LZC primarily isotropically diffuse within intralayer, hindered by higher energy barriers and determined by individual diffusion. The variation in ZrCl62- octahedral unit softening, induced by the specific layered arrangement of zirconium atoms, emerges as a critical determinant of the energy barriers across the LZC phases. These atomic-scale insights into the transport processes provide valuable guidance for the rational design and optimization of LZCs-based electrolytes, accelerating their practical application in advanced energy storage technologies.

cond-mat.mtrl-sci

Exploring Li-ion Transport Properties of Li$_3$TiCl$_6$: A Machine Learning Molecular Dynamics Study

We performed large-scale molecular dynamics simulations based on a machine-learning force field (MLFF) to investigate the Li-ion transport mechanism in cation-disordered Li$_3$TiCl$_6$ cathode at six different temperatures, ranging from 25$^\mathrm{o}$C to 100$^\mathrm{o}$C. In this work, deep neural network method and data generated by $ab-initio$ molecular dynamics (AIMD) simulations were deployed to build a high-fidelity MLFF. Radial distribution functions, Li-ion mean square displacements (MSD), diffusion coefficients, ionic conductivity, activation energy, and crystallographic direction-dependent migration barriers were calculated and compared with corresponding AIMD and experimental data to benchmark the accuracy of the MLFF. From MSD analysis, we captured both the self and distinct parts of Li-ion dynamics. The latter reveals that the Li-ions are involved in anti-correlation motion that was rarely reported for solid-state materials. Similarly, the self and distinct parts of Li-ion dynamics were used to determine Haven's ratio to describe the Li-ion transport mechanism in Li$_3$TiCl$_6$. Obtained trajectory from molecular dynamics infers that the Li-ion transportation is mainly through interstitial hopping which was confirmed by intra- and inter-layer Li-ion displacement with respect to simulation time. Ionic conductivity (1.06 mS/cm) and activation energy (0.29eV) calculated by our simulation are highly comparable with that of experimental values. Overall, the combination of machine-learning methods and AIMD simulations explains the intricate electrochemical properties of the Li$_3$TiCl$_6$ cathode with remarkably reduced computational time. Thus, our work strongly suggests that the deep neural network-based MLFF could be a promising method for large-scale complex materials.

cond-mat.mtrl-sci

Impact of Electron-Withdrawing Groups on Ion Transport and Structure in Lithium Borate Ionic Liquids

Among the distinctive structural features of lithium ionic liquids (LILs), a novel class of single-component electrolytes, the variation of the electron-withdrawing group stands out as a key factor in determining their dynamics. To understand this phenomenon, we conducted molecular dynamics (MD) simulations for LILs based on hexafluoro-2-propanoxy (LIL2), hexafluoro-2-methyl-2-propanoxy (LIL4), and trifluoro-2-propanoxy (LIL6) derivatives. Results revealed that correlated ion dynamics govern the general transport characteristics in LILs, while the electron-withdrawing group regulates the Li transport mechanism. Upon saturation by fluorine atoms, LILs exhibit higher inhomogeneity in their transport and structure properties. Strong coordination along the ethoxide group promotes jumps of Li across positive domains, while in fluorine-poor LILs, stronger coordination in proximity to boron atoms carries the anion along Li transport. Understanding the results of MD simulation will aid the further design and widespread use of this class of electrolytes in production of the energy storage and conversion devices

physics.chem-ph