arXiv subjects

Manuel Grumet

Publications and source records attributed to Manuel Grumet.

9 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↗

arXiv2025

Machine Learning Accelerates Raman Computations from Molecular Dynamics for Materials Science

Raman spectroscopy is a powerful experimental technique for characterizing molecules and materials that is used in many laboratories. First-principles theoretical calculations of Raman spectra are important because they elucidate the microscopic effects underlying Raman activity in these systems. These calculations are often performed using the canonical harmonic approximation which cannot capture certain thermal changes in the Raman response. Anharmonic vibrational effects were recently found to play crucial roles in several materials, which motivates theoretical treatments of the Raman effect beyond harmonic phonons. While Raman spectroscopy from molecular dynamics (MD-Raman) is a well-established approach that includes anharmonic vibrations and further relevant thermal effects, MD-Raman computations were long considered to be computationally too expensive for practical materials computations. In this perspective article, we highlight that recent advances in the context of machine learning have now dramatically accelerated the involved computational tasks without sacrificing accuracy or predictive power. These recent developments highlight the increasing importance of MD-Raman and related methods as versatile tools for theoretical prediction and characterization of molecules and materials.

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↗

arXiv2023

Disentangling the Effects of Structure and Lone-Pair Electrons in the Lattice Dynamics of Halide Perovskites

Metal halide perovskites have shown great performance as solar energy materials, but their outstanding optoelectronic properties are paired with unusually strong anharmonic effects. It has been proposed that this intriguing combination of properties derives from the "lone pair" 6$s^2$ electron configuration of the Pb$^{2+}$ cations, and associated weak pseudo-Jahn-Teller effect, but the precise impact of this chemical feature remains unclear. Here we show that in fact an $ns^2$ electron configuration is not a prerequisite for the strong anharmonicity and low-energy lattice dynamics encountered in this class of materials. We combine X-ray diffraction, infrared and Raman spectroscopies, and first-principles molecular dynamics calculations to directly contrast the lattice dynamics of CsSrBr$_3$ with those of CsPbBr$_3$, two compounds which bear close structural similarity but with the former lacking the propensity to form lone pairs on the 5$s^0$ octahedral cation. We exploit low-frequency diffusive Raman scattering, nominally symmetry-forbidden in the cubic phase, as a fingerprint to detect anharmonicity and reveal that low-frequency tilting occurs irrespective of octahedral cation electron configuration. This work highlights the key role of structure in perovskite lattice dynamics, providing important design rules for the emerging class of soft perovskite semiconductors for optoelectronic and light-harvesting devices.

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arXiv2023

Temperature-transferable tight-binding model using a hybrid-orbital basis

Finite-temperature calculations are relevant for rationalizing material properties yet they are computationally expensive because large system sizes or long simulation times are typically required. Circumventing the need for performing many explicit first-principles calculations, tight-binding and machine-learning models for the electronic structure emerged as promising alternatives, but transferability of such methods to elevated temperatures in a data-efficient way remains a great challenge. In this work, we suggest a tight-binding model for efficient and accurate calculations of temperature-dependent properties of semiconductors. Our approach utilizes physics-informed modeling of the electronic structure in form of hybrid-orbital basis functions and numerically integrating atomic orbitals for the distance dependence of matrix elements. We show that these design choices lead to a tight-binding model with a minimal amount of parameters which are straightforwardly optimized using density functional theory or alternative electronic-structure methods. Temperature-transferability of our model is tested by applying it to existing molecular-dynamics trajectories without explicitly fitting temperature-dependent data and comparison to density functional theory. We utilize it together with machine-learning molecular dynamics and hybrid density functional theory for the prototypical semiconductor gallium arsenide. We find that including the effects of thermal expansion on the onsite terms of the tight-binding model is important in order to accurately describe electronic properties at elevated temperatures in comparison to experiment.

cond-mat.mtrl-sci↗

arXiv2023

Delta Machine Learning for Predicting Dielectric Properties and Raman Spectra

Raman spectroscopy is an important characterization tool with diverse applications in many areas of research. We propose a machine learning method for predicting polarizabilities with the goal of providing Raman spectra from molecular dynamics trajectories at reduced computational cost. A linear-response model is used as a first step and symmetry-adapted machine learning is employed for the higher-order contributions as a second step. We investigate the performance of the approach for several systems including molecules and extended solids. The method can reduce training set sizes required for accurate dielectric properties and Raman spectra in comparison to a single-step machine learning approach.

cond-mat.mtrl-sci↗

arXiv2022

Anharmonic Lattice Dynamics in Sodium Ion Conductors

We employ THz-range temperature-dependent Raman spectroscopy and first-principles lattice-dynamical calculations to show that the undoped sodium ion conductors Na$_3$PS$_4$ and isostructural Na$_3$PSe$_4$ both exhibit anharmonic lattice dynamics. The anharmonic effects in the compounds involve coupled host lattice -- Na$^+$ ion dynamics that drive the tetragonal-to-cubic phase transition in both cases, but with a qualitative difference in the anharmonic character of the transition. Na$_3$PSe$_4$ shows almost purely displacive character with the soft modes disappearing in the cubic phase as the change of symmetry shifts these modes to the Raman-inactive Brillouin zone boundary. Na$_3$PS$_4$ instead shows order-disorder character in the cubic phase, with the soft modes persisting through the phase transition and remaining active in Raman in the cubic phase, violating Raman selection rules for that phase. Our findings highlight the important role of coupled host lattice -- mobile ion dynamics in vibrational instabilities that are coincident with the exceptional conductivity in these Na$^+$ ion conductors.

cond-mat.mtrl-sci↗

arXiv2018

Beyond the quasiparticle approximation: Fully self-consistent $GW$ calculations

We present quasiparticle (QP) energies from fully self-consistent $GW$ (sc$GW$) calculations for a set of prototypical semiconductors and insulators within the framework of the projector-augmented wave methodology. To obtain converged results, both finite basis-set corrections and $k$-point corrections are included, and a simple procedure is suggested to deal with the singularity of the Coulomb kernel in the long-wavelength limit, the so called head correction. It is shown that the inclusion of the head corrections in the sc$GW$ calculations is critical to obtain accurate QP energies with a reasonable $k$-point set. We first validate our implementation by presenting detailed results for the selected case of diamond, and then we discuss the converged QP energies, in particular the band gaps, for a set of gapped compounds and compare them to single-shot $G_0W_0$, QP self-consistent $GW$, and previously available sc$GW$ results as well as experimental results.

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