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.

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, methylammonium lead tribromide (MAPbBr3) and methylammonium lead triiodide (MAPbI3), using a dynamic disorder model. Focusing on the role of realspace transport channels, we analyze temporal orbital occupations to assess the impact of material-specific onsite 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 pp pi channel as a critical bottleneck for charge transport and to provide insights into the differences between electron and hole transport across the two materials.

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.

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 the 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 that are straightforwardly optimized using density functional theory or alternative electronic-structure methods. The temperature transferability of our model is tested by applying it to existing molecular-dynamics trajectories without explicitly fitting temperature-dependent data and comparison with 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 with experiment.

The (opto)electronic behavior of semiconductors depends on their (quasi-)free electronic carrier densities. These are regulated by semiconductor doping, i.e., controlled "electronic contamination". For metal halide perovskites (HaPs), the functional materials in several device types, which already challenge some of the understanding of semiconductor properties, this study shows that doping type, density and properties derived from these, are to a first approximation controlled via their surfaces. This effect, relevant to all semiconductors, and already found for some, is very evident for lead (Pb)-HaPs because of their intrinsically low electrically active bulk and surface defect densities. Volume carrier densities for most polycrystalline Pb-HaP films (<1 µm grain diameter) are below those resulting from even < 0.1% of surface sites being electrically active defects. This implies and is consistent with interfacial defects controlling HaP devices in multi-layered structures with most of the action at the two HaP interfaces. Surface and interface passivation effects on bulk electrical properties are relevant to all semiconductors and are crucial for developing those used today. However, because bulk dopant introduction in HaPs at controlled ppm levels for electronic-relevant carrier densities is so difficult, passivation effects are vastly more critical and dominate, to first approximation, their optoelectronic characteristics in devices.

Ternary nitride semiconductors are rapidly emerging as a promising class of materials for energy conversion applications, offering an appealing combination of strong light absorption in the visible range, desirable charge transport characteristics, and good chemical stability. In this work, it is shown that finite-temperature lattice dynamics in CuTaN2 - a prototypical ternary nitride displaying particularly strong visible light absorption - exhibit a pronounced anharmonic character that plays an essential role in defining its macroscopic optoelectronic and thermal properties. Low-frequency vibrational modes that are Raman-inactive from symmetry considerations of the average crystal structure and unstable in harmonic phonon calculations are found to appear as intensive Raman features near room temperature. The atomic contributions to the anharmonic vibrations are characterized by combining Raman measurements with molecular dynamics and density functional theory calculations. This analysis reveals that anharmonic lattice dynamics have large ramifications on the fundamental properties of this compound, resulting in uniaxial negative thermal expansion and the opening of its bandgap to a near-optimal value for solar energy harvesting. The atomic-level understanding of anharmonic lattice dynamics, as well as the finding that they strongly influence key properties of this semiconductor at room temperature, have important implications for design of new functional materials, especially within the emerging class of ternary nitride semiconductors.

Ternary nitrides are rapidly emerging as promising compounds for optoelectronic and energy conversion applications, yet comparatively little of this vast composition space has been explored. Furthermore, the crystal structures of these compounds can exhibit a significant amount of disorder, the consequences of which are not yet well understood. Here, the deposition of bixbyite-type ZrTaN3 thin films is demonstrated by reactive magnetron co-sputtering and observed semiconducting character, with a strong optical absorption onset at 1.8 eV and significant photoactivity, with prospective application as functional photoanodes. It is found that Wyckoff-site occupancy of cations is a critical factor in determining these beneficial optoelectronic properties. First-principles calculations show that cation disorder leads to minor deviations in the total energy but modulates the bandgap by 0.5 eV, changing orbital hybridization of valence and conduction band states. In addition to demonstrating that ZrTaN3 is a promising visible light-absorbing semiconductor and active photoanode material, the findings provide important insights regarding the role of cation ordering on the electronic structure of ternary semiconductors. In particular, it is shown that not only cation order, but also the cationic Wyckoff site occupancy has a substantial impact on key optoelectronic properties, which can guide future design and synthesis of advanced semiconductors.

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.

Solar Energy Harvesting In article number 2303059, Omer Yaffe, Ian D. Sharp, David A. Egger, and co-workers uncover pronounced anharmonic vibrational effects in the ternary nitride semiconductor, CuTaN2. These dynamic structural characteristics are found to have a large impact on the fundamental properties of this compound, causing an opening of its bandgap to a near-optimal value for solar energy harvesting.

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.

The successful design of solid-state photo- and electrochemical devices depends on the careful engineering of point defects in solid-state ion conductors. Characterization of point defects is critical to these efforts, but the best-developed techniques are difficult and time-consuming. Raman spectroscopy-with its exceptional speed, flexibility, and accessibility-is a promising alternative. Raman signatures arise from point defects due to local symmetry breaking and structural distortions. Unfortunately, the assignment of these signatures is often hampered by a shortage of reference compounds and corresponding reference spectra. This issue can be circumvented by calculation of defect-induced Raman signatures from first principles, but this is computationally demanding. Here, we introduce an efficient computational procedure for the prediction of point defect Raman signatures in solid-state ion conductors. Our method leverages machine-learning force fields and "atomic Raman tensors", i.e., polarizability fluctuations due to motions of individual atoms. We find that our procedure reduces computational cost by up to 80% compared to existing first-principles frozen-phonon approaches. These efficiency gains enable synergistic computational-experimental investigations, in our case allowing us to precisely interpret the Raman spectra of Sr(Ti0.94Ni0.06)O3-delta, a model oxygen ion conductor. By predicting Raman signatures of specific point defects, we determine the nature of dominant defects and unravel impacts of temperature and quenching on in situ and ex situ Raman spectra. Specifically, our findings reveal the temperature-dependent distribution and association behavior of oxygen vacancies and nickel substitutional defects. Overall, our approach enables rapid Raman-based characterization of point defects to support defect engineering in novel solid-state ion conductors

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" 6s^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 5s^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.

The determination of depth profiles across interfaces is of primary importance in many scientific and technological areas. Photoemission spectroscopy is in principle well suited for this purpose, yet a quantitative implementation for investigations of liquid-vapor interfaces is hindered by the lack of understanding of electron-scattering processes in liquids. Previous studies have shown, however, that core-level photoelectron angular distributions (PADs) are altered by depth-dependent elastic electron scattering and can, thus, reveal information on the depth distribution of species across the interface. Here, we explore this concept further and show that the experimental anisotropy parameter characterizing the PAD scales linearly with the average distance of atoms along the surface normal obtained by molecular dynamics simulations. This behavior can be accounted for in the low-collision-number regime. We also show that results for different atomic species can be compared on the same length scale. We demonstrate that atoms separated by about 1 Å along the surface normal can be clearly distinguished with this method, achieving excellent depth resolution.

Alloying is widely adopted for tuning the properties of emergent semiconductors for optoelectronic and photovoltaic applications. So far, alloying strategies have primarily focused on engineering bandgaps rather than optimizing charge-carrier transport. Here, we demonstrate that alloying may severely limit charge-carrier transport in the presence of localized charge carriers (e.g., small polarons). By combining reflection-transmission and optical pump-terahertz probe spectroscopy with first-principles calculations, we investigate the interplay between alloying and charge-carrier localization in Cs2AgSbxBi1-xBr6 double perovskite thin films. We show that the charge-carrier transport regime strongly determines the impact of alloying on the transport properties. While initially delocalized charge carriers probe electronic bands formed upon alloying, subsequently self-localized charge carriers probe the energetic landscape more locally, thus turning an alloy's low-energy sites (e.g., Sb sites) into traps, which dramatically deteriorates transport properties. These findings highlight the inherent limitations of alloying strategies and provide design tools for newly emerging and highly efficient semiconductors.

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.

We show that formamidinium-based crystals are distinct from methylammonium-based halide perovskite crystals because their inorganic sublattice exhibits intrinsic local static disorder that coexists with a well-defined average crystal structure. Our study combines terahertz-range Raman scattering with single-crystal X-ray diffraction and first-principles calculations to probe the evolution of inorganic sublattice dynamics 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 structural dynamics and phase transitions at higher temperatures.

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.

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.

We investigate the role of preaggregation among photocatalystandsubstrate in the paradigmatic molecular dye rhodamine 6G by meansof fluorescence correlation experiments and quantum-mechanical modeling.By varying the substrate concentration, we experimentally confirmearlier conclusions that consecutive photoelectron transfer betweenrhodamine 6G and a model substrate molecule is not diffusion limited,raising puzzling questions regarding the mechanisms underlying itsestablished photocatalytic activity. By theoretically exploring alternativeexplanations for ultrafast excited-state charge transfer, it is indeedfound that preaggregation between photocatalyst and substrate is requiredto reach charge-transfer rates similar to those experimentally observed.Electrostatic as well as dispersive interactions are found to be mostimportant for the molecular attraction involved in preaggregation.We show that tuning these contributions by chemical design altersthe binding energies of photocatalyst-substrate assemblies.This suggests that reaction rates can be adjusted by adapting thecomposition of the species involved in preaggregation, which appearsas an appealing concept in photocatalysis.