arXiv subjects

Takeru Miyagawa

Publications and source records attributed to Takeru Miyagawa.

3 recordsLinked to original sources

arXiv2026

Raman Signatures of Lithium Ion Dynamics in LLZO Garnet Electrolytes: Atomistic Insights from MD-Raman Calculations

Lithium lanthanum zirconate (LLZO) garnets are among the most promising solid electrolytes for next-generation batteries owing to their high ionic conductivity, chemical stability, and compatibility with lithium metal. Raman spectroscopy is commonly employed to distinguish the highly conductive cubic phase from the poorly conductive tetragonal phase of LLZO, yet the atomistic origin of these spectral differences and their direct connection to Li-ion transport remain unresolved. Here, we close this gap by comparing computed and experimental Raman spectra for the tetragonal, cubic, and Ta-doped variants of LLZO, with the computed spectra obtained from the MD-Raman approach that combines machine-learning molecular dynamics with first-principles polarizability calculations. We show that the contrasting ionic transport behavior across these LLZO variants is encoded in the vibrational dynamics of the lithium sublattice and gives rise to distinct features in their Raman spectra. A symmetry-resolved analysis further reveals that experimentally observed Raman peaks do not correspond to individual normal modes, but instead arise from overlapping contributions of multiple symmetry-allowed vibrations, challenging conventional peak-assignment approaches. By explicitly connecting experimentally accessible Raman signatures to the underlying atomic-scale dynamics, our results show how Raman spectroscopy can move beyond empirical phase identification toward a microscopic probe of Li-ion dynamics in lithium garnet electrolytes.

cond-mat.mtrl-sci↗

arXiv2025

Revealing Fast Ionic Conduction in Solid Electrolytes through Machine Learning Accelerated Raman Calculations

Fast ionic conduction is a defining property of solid electrolytes for all-solid-state batteries. Previous studies have suggested that liquid-like cation motion associated with fast ionic transport can disrupt crystalline symmetry, thereby lifting Raman selection rules. Here, we exploit the resulting low-frequency, diffusive Raman scattering as a spectral signature of fast ionic conduction and develop a machine learning-accelerated computational pipeline to identify promising solid electrolytes based on this feature. By overcoming the steep computational barriers to calculating Raman spectra of strongly disordered materials at finite temperatures, we achieve near-ab initio accuracy and demonstrate the predictive power of our approach for sodium-ion conductors, revealing clear Raman signatures of liquid-like ion conduction. This work highlights how machine learning can bridge atomistic simulations and experimental observables, enabling data-efficient discovery of fast-ion conductors.

cond-mat.mtrl-sci↗

arXiv2024

Accurate Description of Ion Migration in Solid-State Ion Conductors from Machine-Learning Molecular Dynamics

Solid-state ion conductors (SSICs) have emerged as a promising material class for electrochemical storage devices and novel compounds of this kind are continuously being discovered. High-throughout approaches that enable a rapid screening among the plethora of candidate SSIC compounds have been essential in this quest. While first-principles methods are routinely exploited in this context to provide atomic-level details on ion migration mechanisms, dynamic calculations of this type are computationally expensive and limit us in the time- and length-scales accessible during the simulations. Here, we explore the potential of recently developed machine-learning force fields for predicting different ion migration mechanisms in SSICs. Specifically, we systematically investigate three classes of SSICs that all exhibit complex ion dynamics including vibrational anharmonicities: AgI, a strongly disordered Ag$^+$ conductor; Na$_3$SbS$_4$, a Na$^+$ vacancy conductor; and Li$_{10}$GeP$_2$S$_{12}$, which features concerted Li$^+$ migration. Through systematic comparison with \textit{ab initio} molecular dynamics data, we demonstrate that machine-learning molecular dynamics provides very accurate predictions of the structural and vibrational properties including the complex anharmonic dynamics in these SSICs. The \textit{ab initio} accuracy of machine-learning molecular dynamics simulations at relatively low computational cost open a promising path toward the rapid design of novel SSICs.

cond-mat.mtrl-sci↗