arXiv subjects

David A. Egger

Publications and source records attributed to David A. Egger.

At least 19 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↗

arXiv2026

Interplay between Electronic Structure, Chemical Bonding, and Lattice Symmetry in Bismuth Vanadate

Bismuth vanadate (BiVO$_4$) is a prototypical oxide photocatalyst that occurs in both tetragonal and monoclinic scheelite phases with markedly different photocatalytic and photoelectrochemical activities. Accurately identifying the monoclinic phase as the ground state and explaining the origin of its symmetry-breaking distortion are unusually challenging from a theoretical perspective, with various levels of theory and associated physical interpretations for this behaviour reported in the literature. Here, we resolve these discrepancies by systematically assessing the role of exact exchange with and without spin-orbit coupling, demonstrating that an accurate treatment of electronic localization is essential to stabilize the monoclinic scheelite structure. Using this framework, we compute the electronic band structure through dense sampling of the Brillouin zone and show that the band edges in monoclinic and tetragonal BiVO$_4$ lie far from conventional high-symmetry paths, leading to substantial differences in band gaps and carrier effective masses. Choosing the exchange-correlation functional that best reproduces the crystal structure leads to excellent predictions of the band gap once excitonic and thermal effects are taken into account. In addition, we show that the monoclinic distortion is driven by charge transfer between non-equivalent oxygen sites, which breaks the lattice symmetry and is suppressed by self-interaction errors when using semi-local DFT. These results establish a direct connection between the exchange-correlation functional, electronic localization, chemical bonding, and structural stability in BiVO$_4$, providing a foundation for robust ab initio descriptions of phase stability and optoelectronic properties in such complex oxides.

cond-mat.mtrl-sci↗

arXiv2026

Physics-informed Hamiltonian learning for large-scale optoelectronic property prediction

Predicting optoelectronic properties of large-scale atomistic systems under realistic conditions is crucial for rational materials design, yet computationally prohibitive with first-principles simulations. Recent neural network models have shown promise in overcoming these challenges, but typically require large datasets and lack physical interpretability. Physics-inspired approximate models offer greater data efficiency and intuitive understanding, but often sacrifice accuracy and transferability. Here we present HAMSTER, a physics-informed machine learning framework for predicting the quantum-mechanical Hamiltonian of complex chemical systems. Starting from an approximate model encoding essential physical effects, HAMSTER captures the critical influence of dynamic environments on Hamiltonians using only few explicit first-principles calculations. We demonstrate our approach on halide perovskites, achieving accurate prediction of optoelectronic properties across temperature and compositional variations, and scalability to systems containing tens of thousands of atoms. This work highlights the power of physics-informed Hamiltonian learning for accurate and interpretable optoelectronic property prediction in large, complex systems.

cond-mat.mtrl-sci↗

arXiv2025

Ultrafast light-induced formation of a metastable hidden state in bismuth vanadate

Bismuth vanadate (BiVO$_4$) is a key photocatalyst for solar fuel applications, yet fundamental questions remain regarding the nature of photogenerated polaronic states and the lattice dynamics that govern its light-to-chemical pathways. Here, we use femtosecond optical pump-X-ray probe measurements to track the photoinduced electronic and structural dynamics in BiVO$_4$ across multiple length and time scales. Transient X-ray absorption spectroscopy captures sub-picosecond electron localization within VO$_4$ tetrahedra, consistent with small polaron formation, whereas time-resolved X-ray diffraction reveals a slower, multi-picosecond lattice reorganization into a hidden photoexcited state that is structurally distinct from both the monoclinic ground state and the high-temperature tetragonal phase. Supported by density functional theory, we show that hole-lattice interactions dynamically reduce the ground state monoclinic distortion, stabilizing the hidden state. Our results demonstrate that electron- and hole-lattice coupling jointly shape the excited state landscape, with implications for carrier transport, interfacial energetics, and light-to-chemical energy conversion pathways.

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

Predicting the Thermal Behavior of Semiconductor Defects with Equivariant Neural Networks

The presence of defects strongly influences semiconductor behavior. However, predicting the electronic properties of defective materials at finite temperatures remains computationally expensive even with density functional theory due to the large number of atoms in the simulation cell and the multitude of thermally accessible configurations. Here, we present a neural network-based framework to investigate the electronic properties of defective semiconductors at finite temperatures efficiently. We develop an active learning approach that integrates two advanced equivariant graph neural networks: MACE for atomic energies and forces and DeepH-E3 for the electronic Hamiltonian. Focusing on representative point defects in GaAs, we demonstrate computational accuracy comparable to density functional theory at a fraction of the computational cost, predicting the temperature-dependent band gap of defective GaAs directly from larger scale molecular dynamics trajectories with an accuracy of few tens of meV. Our results highlight the potential of equivariant neural networks for accurate atomic-scale predictions in complex, dynamically evolving materials.

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↗

arXiv2025

Analysis of real-space transport channels for electrons and holes in halide perovskites

Predicting and explaining charge carrier transport in halide perovskites is a formidable challenge because of the unusual vibrational and electron-phonon coupling properties of these materials. This study explores charge carrier transport in two prototypical halide perovskite materials, MAPbBr$_3$ and MAPbI$_3$, using a dynamic disorder model. Focusing on the role of real-space transport channels, we analyze temporal orbital occupations to assess the impact of material-specific on-site energy levels and spin-orbit coupling (SOC) strengths. Our findings reveal that both on-site energies and SOC magnitude significantly influence the orbital occupation dynamics, thereby affecting charge dispersal and carrier mobility. In particular, energy gaps across on-site levels and the halide SOC strength govern the filling of transport channels over time. This leads us to identify the $ppπ$ channel as a critical bottleneck for charge transport and to provide insights into the differences between electron and hole transport across the two materials.

cond-mat.mtrl-sci↗

arXiv2025

Machine-Learning Force Fields Reveal Shallow Electronic States on Dynamic Halide Perovskite Surfaces

The spectacular performance of halide perovskites in optoelectronic devices is rooted in their tolerance to defects. Previous studies showed that defects in these materials generate shallow electronic states. However, how these shallow states persist amid the pronounced atomic dynamics on halide perovskite surfaces remains unknown. This work reveals that electronic states at surfaces of prototypical CsPbBr$_3$ are energetically distributed at room temperature akin to well-passivated inorganic semiconductors, even when covalent bonds remain cleaved and undercoordinated. Specifically, a striking tendency for shallow surface states is found with approximately 70% of surface-state energies appearing within 0.2 eV or ${\approx}8k_\text{B}T$ from the valence-band edge. While these findings do not rule out occurrence of deep traps per se, they show that even when surface states appear deeper in the gap, they are not energetically isolated and are less likely to act as traps. We achieve this result by accelerating first-principles calculations via machine learning and show that the unique atomic dynamics in these materials render the formation of deep electronic states at their surfaces unlikely. These findings reveal the microscopic mechanism behind the low density of deep states at dynamic halide perovskite surfaces, which is key to their device performance.

cond-mat.mtrl-sci↗

arXiv2024

The Effect of Overdamped Phonons on the Fundamental Band Gap of Perovskites

Anharmonic atomic motions can strongly influence the optoelectronic properties of materials but how these effects are connected to the underlying phonon band structure is not understood well. We investigate how the electronic band gap is influenced by overdamped phonons, which occur in an intriguing regime of phonon-phonon interactions where vibrational lifetimes fall below one oscillation period. We contrast the anharmonic halide perovskite CsPbBr$_3$, known to exhibit overdamped phonons in its cubic phase, with the anharmonic oxide perovskite SrTiO$_3$ where the phonons are underdamped at sufficiently high temperatures. Our results show that overdamped phonons strongly impact the band gap and cause slow dynamic fluctuations of electronic levels that have been implicated in the unique optoelectronic properties of halide perovskites. This finding is enabled by developing augmented stochastic Monte Carlo methods accounting for phonon renormalization and imaginary modes that are typically neglected. Our work provides guidelines for capturing anharmonic effects in theoretical calculations of materials.

cond-mat.mtrl-sci↗

arXiv2024

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↗

arXiv2024

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↗

arXiv2024

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.

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

Anharmonic Fluctuations Govern the Band Gap of Halide Perovskites

We determine the impact of anharmonic thermal vibrations on the fundamental band gap of CsPbBr$_3$, a prototypical model system for the broader class of halide perovskite semiconductors. Through first-principles molecular dynamics and stochastic calculations, we find that anharmonic fluctuations are a key effect in the electronic structure of these materials. We present experimental and theoretical evidence that important characteristics, such as a mildly changing band-gap value across a temperature range that includes phase-transitions, cannot be explained by harmonic phonons thermally perturbing an average crystal structure and symmetry. Instead, the thermal characteristics of the electronic structure are microscopically connected to anharmonic vibrational contributions to the band gap that reach a fairly large magnitude of 450 meV at 425 K.

cond-mat.mtrl-sci↗

arXiv2023

Correlated Anharmonicity and Dynamic Disorder Control Carrier Transport in Halide Perovskites

Halide pervoskites are an important class of semiconducting materials which hold great promise for optoelectronic applications. In this work we investigate the relationship between vibrational anharmonicity and dynamic disorder in this class of solids. Via a multi-scale model parameterized from first-principles calculations, we demonstrate that the non-Gaussian lattice motion in halide perovskites is microscopically connected to the dynamic disorder of overlap fluctuations among electronic states. This connection allows us to rationalize the emergent differences in temperature-dependent mobilities of prototypical MAPbI$_3$ and MAPbBr$_3$ compounds across structural phase-transitions, in agreement with experimental findings. Our analysis suggests that the details of vibrational anharmonicity and dynamic disorder can complement known predictors of electronic conductivity and can provide structure-property guidelines for the tuning of carrier transport characteristics in anharmonic semiconductors.

cond-mat.mtrl-sci↗

arXiv2022

Static and Dynamic Disorder in Formamidinium Lead Bromide Single Crystals

We show that formamidinium lead bromide is unique among the halide perovskite crystals because its inorganic sub-lattice exhibits intrinsic local static disorder that co-exists with a well-defined average crystal structure. Our study combines THz-range Raman-scattering with single-crystal X-ray diffraction and first-principles calculations to probe the inorganic sub-lattice dynamics evolution with temperature in the range of 10-300 K. The temperature evolution of the Raman spectra shows that low-temperature, local static disorder strongly affects the crystal's structural dynamics and phase transitions at higher temperatures.

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↗