arXiv subjects

Selva Chandrasekaran Selvaraj

Publications and source records attributed to Selva Chandrasekaran Selvaraj.

7 recordsLinked to original sources

arXiv2026

Atomic-Scale Mechanisms of Li-Ion Transport Mediated by Li10GeP2S12 in Composite Solid Polyethylene Oxide Electrolytes

Polymer electrolytes incorporating Li$_{10}$GeP$_{2}$S$_{12}$ (LGPS) nanoparticles show promise for solid-state lithium batteries owing to their enhanced ionic conductivity, though the governing mechanisms remain unclear. We combine molecular dynamics (MD) simulations, experimental ionic conductivity measurements, and density functional theory (DFT) calculations to elucidate the effect of LGPS loading on polyethylene oxide (PEO) structure and Li-ion transport. MD and experimental results agree up to 10\% LGPS, showing a volcano-shaped conductivity trend driven by polymer segmental dynamics and interfacial effects. Beyond 10\%, experiments reveal additional conductivity enhancement unexplained by MD, suggesting a distinct transport regime. DFT calculations indicate that Li-ion migration at the PEO|LGPS interface proceeds via vacancy-mediated hopping, with low barriers favored by S-rich interfacial sites and hindered by Ge. These findings link interfacial chemistry and microstructure to Li-ion dynamics, offering guidelines for designing high-performance composite polymer electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Ionic Interdiffusion at Cathode-Solid-Electrolyte Interface: A Machine Learning-Assisted Multiscale Investigation and Mitigation Strategies

Future lithium-based batteries are expected to use solid electrolytes to achieve higher energy density and fast charge capabilities. The majority of solid electrolytes are thermodynamically unstable against layered oxide cathodes. Here, the stability of LiCoO2 (LCO) cathode with Li10GeP2S12 (LGPS) solid electrolyte is investigated using ab initio molecular dynamics (AIMD) and machine learning molecular dynamics (MLMD). The propensity of ionic interdiffusion, formation of a passivation layer, and corresponding decay in cell performance is addressed using a continuum model. The large-scale MLMD simulations confirm that the LCO|LGPS interface permits interdiffusion of Co and other ionic species, leading to the formation and growth of a resistive interphase and dramatic capacity fade even in the first cycle. We then examine the literature evidence that incorporating a thin layer of LiNb0.5Ta0.5O3 (LNTO) between LCO and LGPS prevents the interdiffusion of ions. Atomistic simulations suggest that the substitution of Li in LNTO with Co is not thermodynamically favorable, which helps to minimize the ionic interdiffusion process. The stable Nb/Ta5+ states form a rigid metal-oxide framework, which consequently also prevents the substitution of Nb/Ta. However, continuum level analysis suggests that due to the higher mechanical stiffness of LNTO, interfacial delamination between the LCO and LNTO is possible, which can minimize the effectiveness of the protective layer. This paper suggests the need for the development of novel interlayers that balance low interdiffusion with low stiffness.

cond-mat.mtrl-sci↗

arXiv2025

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↗

arXiv2025

Interactive Multiscale Modeling to Bridge Atomic Properties and Electrochemical Performance in Li-CO$_2$ Battery Design

Li-CO$_2$ batteries are promising energy storage systems due to their high theoretical energy density and CO$_2$ fixation capability, relying on reversible Li$_2$CO$_3$/C formation during discharge/charge cycles. We present a multiscale modeling framework integrating Density Functional Theory (DFT), Ab-Initio Molecular Dynamics (AIMD), classical Molecular Dynamics (MD), and Finite Element Analysis (FEA) to investigate atomic and cell-level properties. The considered Li-CO$_2$ battery consists of a lithium metal anode, an ionic liquid electrolyte, and a carbon cloth cathode with Sb$_{0.67}$Bi$_{1.33}$Te$_3$ catalyst. DFT and AIMD determined the electrical conductivities of Sb$_{0.67}$Bi$_{1.33}$Te$_3$ and Li$_2$CO$_3$ using the Kubo-Greenwood formalism and studied the CO$_2$ reduction mechanism on the cathode catalyst. MD simulations calculated the CO$_2$ diffusion coefficient, Li$^+$ transference number, ionic conductivity, and Li$^+$ solvation structure. The FEA model, parameterized with atomistic simulations data, reproduced the available experimental voltage-capacity profile at 1 mA/cm$^2$ and revealed spatio-temporal variations in Li$_2$CO$_3$/C deposition, porosity, and CO$_2$ concentration dependence on discharge rates in the cathode. Accordingly, Li$_2$CO$_3$ can form large and thin film deposits, leading to dispersed and local porosity changes at 0.1 mA/cm$^2$ and 1 mA/cm$^2$, respectively. The capacity decreases exponentially from 81,570 mAh/g at 0.1 mA/cm$^2$ to 6,200 mAh/g at 1 mA/cm$^2$, due to pore clogging from excessive discharge product deposition that limits CO$_2$ transport to the cathode interior. Therefore, the performance of Li-CO$_2$ batteries can be improved by enhancing CO$_2$ transport, regulating Li$_2$CO$_3$ deposition, and optimizing cathode architecture.

cond-mat.mtrl-sci↗

arXiv2025

Mechanisms and Stability of Li Dynamics in Amorphous Li-Ti-P-S-Based Mixed Ionic-Electronic Conductors: A Machine Learning Molecular Dynamics Study

Mixed ionic-electronic conductors (MIECs) exhibit both high ionic and electronic conductivity to improve the battery performance. In this work, we investigate the mechanism and stability of transport channels in our recently developed MIEC material, amorphous Ti-doped lithium phosphorus sulfide (LPS), using molecular dynamics (MD) simulations with a 99\% accurate machine-learning force field (MLFF) trained on \textit{ab-initio} MD data. The achieved MLFF helps efficient large-scale MD simulations on LPS with three Ti concentrations (10\%, 20\%, and 30\%) and six temperatures (25$^\mathrm{o}$C to 225$^\mathrm{o}$C) to calculate ionic conductivity, activation energy, Li-ion transport mechanism, and configurational entropy. Results show that ionic conductivities and activation energies are consistent with our recent experimental values. Moreover, Li-ion transport occurs via free-volume diffusion facilitated by the formation of disordered Li-S polyhedra. The enhanced stability of transport channels at 10\% and 20\% Ti doping, compared to 0\% and 30\%, is observed by analyzing the vibrational and configurational entropy of these disordered Li-S polyhedra. Overall, this study highlights the utility of MLFF-based large-scale MD simulations in explaining the transport mechanism and the stability of Li-ion in Ti-doped LPS electrolyte with significant computational efficiency.

cond-mat.mtrl-sci↗

arXiv2024

Graph Neural Networks Based Deep Learning for Predicting Structural and Electronic Properties

This study presents a deep learning approach to predicting structural and electronic properties of materials using Graph Neural Networks (GNNs). Leveraging data from the Materials Project database, we construct graph representations of crystal structures and employ GNNs to predict multiple properties simultaneously. All crystal structures are from the Materials Project database, with a total of 158,874 structures used. Our model achieves high predictive accuracy across various properties, as indicated by $ R^2 $ values: 0.96 for density, 0.97 for formation energy, 0.54 for energy above hull, 0.47 for structural stability (is\_S), 0.76 for band gap, 0.86 for valence band maximum, 0.78 for conduction band minimum, and 0.82 for Fermi energy. These results demonstrate the potential of GNNs in materials science, offering a powerful tool for rapid screening and discovery of materials with desired properties.

cond-mat.dis-nn↗

arXiv2024

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↗