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Chalcogenide perovskites are lead-free materials for potential photovoltaic or thermoelectric applications. BaZrS$_3$ is the most studied member of this family due to its superior thermal and chemical stability, desirable optoelectronic properties, and low thermal conductivity. Phase transitions of the BaZrS$_3$ perovskite are under-explored in literature as most experimental characterization is performed at ambient conditions where the orthorhombic Pnma phase is reported to be stable. In this work, we study the dynamics of BaZrS$_3$ across a range of temperatures and pressures using an accurate machine-learned interatomic potential trained with data from hybrid density functional theory calculations. At 0Pa, we find a first-order phase transition from the orthorhombic to tetragonal I4/mcm phase at 610K, and a second-order transition from the tetragonal to the cubic Pm-3m phase at 880K. The tetragonal phase is stable over a larger temperature range at higher pressures. To confirm the validity of our model we report the static structure factor as a function of temperature and compare our results with published experimental data.

Raman spectroscopy is a widely used experimental technique to study the vibrational properties of solids. Atomic scale simulations can be used to predict such spectra, but trustworthy studies at finite temperatures are challenging, mainly due to the requirement of accurate and computationally efficient models for the dielectric susceptibility. Here, we have made use of molecular dynamics (MD) simulations together with a density functional theory (DFT) based model for the dielectric susceptibility to determine the Raman spectrum of barium zirconate, BaZrO$_3$ (BZO), a well-studied oxide perovskite. At ambient conditions, where the system is cubic, we find excellent agreement with experimentally measured Raman spectra. Our study establishes that the relatively sharp spectra seen experimentally are due to second-order scattering. At higher pressures, where BZO is tetragonal, all first-order Raman active modes are identified. Additionally, slightly below the phase transition, in the cubic phase, a broad "central Raman peak" appears. The origin of this type of peak is controversial and extensively debated in connection to the dynamics of the halide perovskites. Here, we show that it is also present in a "hard" oxide perovskite, and it originates from the highly overdamped R-tilt mode in the cubic structure.

Path-integral molecular dynamics (PIMD) simulations are crucial for accurately capturing nuclear quantum effects in materials. However, their computational intensity and reliance on multiple software packages often limit their applicability at large scales. Here, we present an integration of PIMD methods, including thermostatted ring-polymer molecular dynamics (TRPMD), into the open-source GPUMD package, combined with highly accurate and efficient machine-learned neuroevolution potential (NEP) models. This approach achieves almost the accuracy of first-principles calculations with the computational efficiency of empirical potentials, enabling large-scale atomistic simulations that incorporate nuclear quantum effects. We demonstrate the efficacy of the combined NEP-PIMD approach by examining various thermal properties of diverse materials, including lithium hydride (LiH), three porous metal-organic frameworks (MOFs), liquid water, and elemental aluminum. For LiH, our NEP-PIMD simulations successfully capture the isotope effect, reproducing the experimentally observed dependence of the lattice parameter on the reduced mass. For MOFs, our results reveal that achieving good agreement with experimental data requires consideration of both nuclear quantum effects and dispersive interactions. For water, our PIMD simulations capture the significant impact of nuclear quantum effects on its microscopic structure. For aluminum, the TRPMD method effectively captures thermal expansion and phonon properties, aligning well with quantum mechanical predictions. This efficient NEP-PIMD approach opens new avenues for exploring complex material properties influenced by nuclear quantum effects, with potential applications across a broad range of materials., Comment: 16 pages, 9 figures in the main text; 1 table and 8 figures in the SI

Color centers play key roles in applications, including, e.g., solid state lighting and quantum information technology, for which the coupling between their optical and vibrational properties is crucial. Established methodologies for predicting the optical lineshapes of such emitters rely on the generating function (GF) approach and impose tight constraints on the shape of and relationship between the ground and excited state landscapes, which limits their application range. Here, we describe an approach based on direct sampling of the underlying auto-correlation functions through molecular dynamics simulations (MD-ACF) that overcomes these restrictions. The energy landscapes are represented by a machine-learned potential, which provides an accurate yet efficient description of both the ground and excited state landscapes through a single model, guaranteeing size-consistent predictions. We apply this methodology to the (VSiVC)kk(0) divacancy defect in 4H-SiC, a prototypical color center, which has been studied both experimentally and theoretically. We demonstrate that at low temperatures the present MD-ACF approach yields predictions in agreement with earlier GF calculations. Unlike the latter it is, however, also applicable at high temperatures as it is not subject to the same limitations, especially with respect to handling of anharmonicity, and can be applied to study non-crystalline materials. While we discuss remaining challenges and possible extensions, the methodology presented here already holds the potential to substantially widen the range of computational predictions of the optical properties of color centers and related defects, especially for cases with pronounced anharmonicity and/or large differences between the initial and final states., Comment: 12 pages, 6 figures

Plasmonic excitations decay within femtoseconds, leaving non-thermal (often referred to as "hot") charge carriers behind that can be injected into molecular structures to trigger chemical reactions that are otherwise out of reach -- a process known as plasmonic catalysis. In this Letter, we demonstrate that strong coupling between resonator structures and plasmonic nanoparticles can be used to control the spectral overlap between the plasmonic excitation energy and the charge injection energy into nearby molecules. Our atomistic description couples real-time density-functional theory self-consistently to Maxwell's equations via the radiation-reaction potential. Control over the resonator provides then an additional knob for non-intrusively enhancing plasmonic catalysis and dynamically reacting to deterioration of the catalyst -- a new facet of modern catalysis.

Cs$_2$AgBiBr$_6$ exhibits promising photovoltaic and light-emitting properties, making it a candidate for next-generation solar cells and LED technologies. Additionally, it serves as a model system within the family of halide double perovskites, offering insights into the broader class of materials. Here, we study various possible excited states of this material to understand its absorption and emission properties. We use Time-Dependent Density Functional Theory (TD-DFT) coupled with non-empirical hybrid functionals, specifically PBE0($\alpha$) and dielectric-dependent hybrids (DDH) to explore direct, indirect, and self-trapped excitons in this material. Based on comparison with experiment, we show that these methods can give excellent prediction of the absorption spectrum and that the fundamental band gap has been underestimated in previous computational studies. We connect the experimental photoluminescence signals at 1.9-2.0 eV to the emission from self-trapped excitons and electron polarons. Finally, we reveal a complex landscape with energetically competing direct, indirect, and self-trapped excitons in the material.

Crystalline alloys and related mixed systems make up a large family of materials with high tunability which have been proposed as the solution to a large number of energy related materials design problems. Due to the presence of chemical order and disorder in these systems, neither experimental efforts nor ab-initio computational methods alone are sufficient to span the inherently large configuration space. Therefore, fast and accurate models are necessary. To this end, cluster expansions have been widely and successfully used for the past decades. Cluster expansions are generalized Ising models designed to predict the energy of any atomic configuration of a system after training on a small subset of the available configurations. Constructing and sampling a cluster expansion consists of multiple steps that have to be performed with care. In this tutorial, we provide a comprehensive guide to this process, highlighting important considerations and potential pitfalls. The tutorial consists of three parts, starting with cluster expansion construction for a relatively simple system, continuing with strategies for more challenging systems such as surfaces and closing with examples of Monte Carlo sampling of cluster expansions to study order-disorder transitions and phase diagrams., Comment: 21 pages; 13 figures

Transition metal dichalcogenide (TMD) materials have attracted substantial interest due to their remarkable excitonic, optical, electrical, and mechanical properties, which are highly dependent on their crystal structure. Controlling the crystal structure of these materials is essential for fine-tuning their performance, $\textit{e.g.}$, linear and nonlinear optical, as well as charge transport properties. While various phase-switching TMD materials, like molybdenum telluride (MoTe$_2$), are available, their transitions are often irreversible. Here, we investigate the mechanism of a light-induced reversible phase transition in mono- and bilayer flakes of rhenium disulfide (ReS$_2$). Our observations, based on scanning transmission electron microscopy, nonlinear spectroscopy, and density functional theory calculations, reveal a transition from the ground T$''$ (double distorted T) to the metastable H$'$ (distorted H) phase under femtosecond laser irradiation or influence of highly-energetic electrons. We show that the formation of sulfur vacancies facilitates this phenomenon. Our findings pave the way towards actively manipulating the crystal structure of ReS$_2$ and possibly its heterostructures.

Two-dimensional (2D) halide perovskites (HPs) are promising materials for various optoelectronic applications, yet a comprehensive understanding of their dynamics is still elusive. Here, we offer insight into the dynamics of prototypical 2D HPs based on MAPbI$_3$ as a function of linker molecule and the number of perovskite layers using atomic scale simulations. We show that the layers closest to the linker undergo transitions that are distinct from those of the interior layers. These transitions can take place anywhere between a few tens of Kelvin below to more than 100 K above the cubic-tetragonal transition of bulk MAPbI$_3$. In combination with the thickness of the perovskite layer this enables one to template phase transitions and tune the dynamics over a wide temperature range. Our results thereby reveal the details of an important and generalizable design mechanism for tuning the properties of these materials., Comment: 7 pages, 5 figures

Small-angle scattering tensor tomography is a technique for studying anisotropic nanostructures of millimeter-sized samples in a volume-resolved manner. It requires the acquisition of data through repeated tomographic rotations about an axis which is subjected to a series of tilts. The tilt that can be achieved with a typical setup is geometrically constrained, which leads to limits in the set of directions from which the different parts of the reciprocal-space map can be probed. Here, we characterize the impact of this limitation on reconstructions in terms of the missing wedge problem of tomography, by treating the problem of tensor tomography as the reconstruction of a three-dimensional field of functions on the unit sphere, represented by a grid of Gaussian radial basis functions. We then devise an acquisition scheme to obtain complete data by remounting the sample, which we apply to a sample of human trabecular bone. Performing tensor tomographic reconstructions of limited data sets as well as the complete data set, we further investigate and validate the missing wedge understanding of data incompleteness by investigating reconstruction errors due to data incompleteness across both real and reciprocal space. Finally, we carry out an analysis of orientations and derived scalar quantities, to quantify the impact of this missing wedge problem on a typical tensor tomographic analysis. We conclude that the effects of data incompleteness are consistent with the predicted impact of the missing wedge problem, and that the impact on tensor tomographic analysis is appreciable but limited, especially if precautions are taken. In particular, there is only limited impact on the means and relative anisotropies of the reconstructed reciprocal space maps., Comment: Main: 32 pages, 9 figures; Supplementary: 9 pages, 7 figuers

The ability to rapidly detect hydrogen gas upon occurrence of a leak is critical for the safe large-scale implementation of hydrogen (energy) technologies. However, to date, no technically viable sensor solution exists that meets the corresponding response time targets set by stakeholders at technically relevant conditions. Here, we demonstrate how a tailored Long Short-term Transformer Ensemble Model for Accelerated Sensing (LEMAS) accelerates the response of a state-of-the-art optical plasmonic hydrogen sensor by up to a factor of 40 in an oxygen-free inert gas environment, by accurately predicting its response value to a hydrogen concentration change before it is physically reached by the sensor hardware. Furthermore, it eliminates the pressure dependence of the response intrinsic to metal hydride-based sensors, while leveraging their ability to operate in oxygen-starved environments that are proposed to be used for inert gas encapsulation systems of hydrogen installations. Moreover LEMAS provides a measure for the uncertainty of the predictions that is pivotal for safety-critical sensor applications. Our results thus advertise the use of deep learning for the acceleration of sensor response, also beyond the realm of plasmonic hydrogen detection., Comment: 11 pages; 5 figures

Layered halide perovskites exhibit remarkable optoelectronic properties and technological promise, driven by strongly bound excitons. The interplay of spin-orbit and exchange coupling creates a rich excitonic landscape, determining their optical signatures and exciton dynamics. Despite the dark excitonic ground state, surprisingly efficient emission from higher-energy bright states has puzzled the scientific community, sparking debates on relaxation mechanisms. Combining low-temperature magneto-optical measurements with sophisticated many-particle theory, we elucidate the origin of the bright exciton emission in perovskites by tracking the thermalization of dark and bright excitons under a magnetic field. We clearly attribute the unexpectedly high emission to a pronounced phonon-bottleneck effect, considerably slowing down the relaxation towards the energetically lowest dark states. We demonstrate that this bottleneck can be tuned by manipulating the bright-dark energy splitting and optical phonon energies, offering valuable insights and strategies for controlling exciton emission in layered perovskite materials that is crucial for optoelectronics applications.

Infrared and Raman spectroscopy are widely used for the characterization of gases, liquids, and solids, as the spectra contain a wealth of information concerning in particular the dynamics of these systems. Atomic scale simulations can be used to predict such spectra but are often severely limited due to high computational cost or the need for strong approximations that limit application range and reliability. Here, we introduce a machine learning (ML) accelerated approach that addresses these shortcomings and provides a significant performance boost in terms of data and computational efficiency compared to earlier ML schemes. To this end, we generalize the neuroevolution potential approach to enable the prediction of rank one and two tensors to obtain the tensorial neuroevolution potential (TNEP) scheme. We apply the resulting framework to construct models for the dipole moment, polarizability, and susceptibility of molecules, liquids, and solids, and show that our approach compares favorably with several ML models from the literature with respect to accuracy and computational efficiency. Finally, we demonstrate the application of the TNEP approach to the prediction of infrared and Raman spectra of liquid water, a molecule (PTAF-), and a prototypical perovskite with strong anharmonicity (BaZrO3). The TNEP approach is implemented in the free and open source software package GPUMD, which makes this methodology readily available to the scientific community., Comment: 11 pages, 6 figures

Altering chemical reactivity and material structure in confined optical environments is on the rise, and yet, a conclusive understanding of the microscopic mechanisms remains elusive. This originates mostly from the fact that accurately predicting vibrational and reactive dynamics for soluted ensembles of realistic molecules is no small endeavor, and adding (collective) strong light-matter interaction does not simplify matters. Here, we establish a framework based on a combination of machine learning (ML) models, trained using density-functional theory calculations, and molecular dynamics to accelerate such simulations. We then apply this approach to evaluate strong coupling, changes in reaction rate constant, and their influence on enthalpy and entropy for the deprotection reaction of 1-phenyl-2-trimethylsilylacetylene, which has been studied previously both experimentally and using ab initio simulations. While we find qualitative agreement with critical experimental observations, especially with regard to the changes in kinetics, we also find differences in comparison with previous theoretical predictions. The features for which the ML-accelerated and ab initio simulations agree show the experimentally estimated kinetic behavior. Conflicting features indicate that a contribution of dynamic electronic polarization to the reaction process is more relevant then currently believed. Our work demonstrates the practical use of ML for polaritonic chemistry, discusses limitations of common approximations and paves the way for a more holistic description of polaritonic chemistry., Comment: Added: detailed evaluation of the vibrational frequencies; reruns of Figure 4 for different coupling values; additional results using a smaller (6-31G*) basis; revised, corrected, and extended Figure 3 with additional discussion (including Si--C contributions); more computational details; overall improvements; NEP model available via Zenodo [https://doi.org/10.5281/zenodo.10255268]

Alloyed metal nanoparticles are a promising platform for plasmonically enabled hot-carrier generation, which can be used to drive photochemical reactions. Although the non-plasmonic component in these systems has been investigated for its potential to enhance catalytic activity, its capacity to affect the photochemical process favorably has been underexplored by comparison. Here, we study the impact of surface alloy species and concentration on hot-carrier generation in Ag nanoparticles. By first-principles simulations, we photoexcite the localized surface plasmon, allow it to dephase, and calculate spatially and energetically resolved hot-carrier distributions. We show that the presence of non-noble species in the topmost surface layer drastically enhances hot-hole generation at the surface at the expense of hot-hole generation in the bulk, due to the additional d-type states that are introduced to the surface. The energy of the generated holes can be tuned by choice of the alloyant, with systematic trends across the d-band block. Already low surface alloy concentrations have a large impact, with a saturation of the enhancement effect typically close to 75% of a monolayer. Hot-electron generation at the surface is hindered slightly by alloying but here an judicious choice of the alloy composition allows one to strike a balance between hot electrons and holes. In this context, it is also important to consider that increasing the alloy concentration broadens the localized surface plasmon resonance, and thus decreases hot-carrier generation overall. Our work underscores the promise of utilizing multicomponent nanoparticles to achieve enhanced control over plasmonic catalysis, and provides guidelines for how hot-carrier distributions can be tailored by designing the electronic structure of the surface through alloying., Comment: 9 pages, 6 figures

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science., Comment: Main text with 17 pages and 8 figures; supplementary with 26 figures and 4 tables; source code and training/test data available

We review the GPAW open-source Python package for electronic structure calculations. GPAW is based on the projector-augmented wave method and can solve the self-consistent density functional theory (DFT) equations using three different wave-function representations, namely real-space grids, plane waves, and numerical atomic orbitals. The three representations are complementary and mutually independent and can be connected by transformations via the real-space grid. This multi-basis feature renders GPAW highly versatile and unique among similar codes. By virtue of its modular structure, the GPAW code constitutes an ideal platform for implementation of new features and methodologies. Moreover, it is well integrated with the Atomic Simulation Environment (ASE) providing a flexible and dynamic user interface. In addition to ground-state DFT calculations, GPAW supports many-body GW band structures, optical excitations from the Bethe-Salpeter Equation (BSE), variational calculations of excited states in molecules and solids via direct optimization, and real-time propagation of the Kohn-Sham equations within time-dependent DFT. A range of more advanced methods to describe magnetic excitations and non-collinear magnetism in solids are also now available. In addition, GPAW can calculate non-linear optical tensors of solids, charged crystal point defects, and much more. Recently, support of GPU acceleration has been achieved with minor modifications of the GPAW code thanks to the CuPy library. We end the review with an outlook describing some future plans for GPAW.

Transition metal dichalcogenides are investigated for various applications at the nanoscale thanks to their unique combination of properties and dimensionality. For many of the anticipated applications, heat conduction plays an important role. At the same time, these materials often contain relatively large amounts of point defects. Here, we provide a systematic analysis of the impact of intrinsic and selected extrinsic defects on the lattice thermal conductivity of MoS$_2$ and WS$_2$ monolayers. We combine Boltzmann transport theory and the Green's function-based T-matrix approach for the calculation of scattering rates. The force constants for the defect configurations are obtained from density functional theory calculations via a regression approach, which allows us to sample a rather large number of defects at a moderate computational cost and to systematically enforce both the translational and rotational acoustic sum rules. The calculated lattice thermal conductivity is in quantitative agreement with experimental data for heat transport and defect concentrations for both MoS$_2$ and WS$_2$. Crucially, this demonstrates that the strong deviation from a 1/T-temperature dependence of the lattice thermal conductivity observed experimentally, can be fully explained by the presence of point defects. We furthermore predict the scattering strengths of the intrinsic defects to decrease in the sequence $V_{Mo}\approx V_{2S}^=>V_{2S}^\perp>V_S>S_{ad}$ in both materials, while the scattering rates for the extrinsic (adatom) defects decrease with increasing mass such that Li$_{ad}$>Na$_{ad}$>K$_{ad}$. Compared to earlier work, we find that both intrinsic and extrinsic adatoms are relatively weak scatterers. We attribute this difference to the treatment of the translational and rotational acoustic sum rules, which if not enforced can lead to spurious contributions in the zero-frequency limit., Comment: 8 pages, 4 figures

Barium zirconate BaZrO3 is one of few perovskites that is claimed to retain an average cubic structure down to 0K at ambient pressure, while being energetically very close to a tetragonal phase obtained by condensation of a soft phonon mode at the R-point. Previous studies suggest, however, that the local structure of BaZrO3 may change at low temperature forming nanodomains or a glass-like phase. Here, we investigate the global and local structure of BaZrO3 as a function of temperature and pressure via molecular dynamics simulations using a machine-learned potential with near density functional theory (DFT) accuracy. We show that the softening of the octahedral tilt mode at the R-point gives rise to weak diffuse superlattice reflections at low temperatures and ambient pressure, which are also observed experimentally. However, we do not observe any static nanodomains but rather soft dynamic fluctuations of the ZrO6 octahedra with a correlation length of 2 to 3nm over time-scales of about 1ps. This soft dynamic behaviour is the precursor of a phase transition and explains the emergence of weak superlattice peaks in measurements. On the other hand, when increasing the pressure at 300K we find a phase transition from the cubic to the tetragonal phase at around 16GPa, also in agreement with experimental studies.

Halide perovskites have emerged as a promising class of materials for photovoltaic applications. A challenge in these applications is how to prevent the crystal structure from degradation to photovoltaically inactive phases, which requires an understanding of the free energy landscape of these materials. Here, we uncover the free energy landscape of two prototypical halide perovskites, CsPbBr$_3$ and MAPbI$_3$ via atomic scale simulations using umbrella sampling and machine-learned potentials. For CsPbBr$_3$ we find very small free energy differences and barriers close to the transition temperatures for both the tetragonal-to-cubic and the orthorhombic-to-tetragonal transition. For MAPbI$_3$, however, the situation is more intricate. In particular the orthorhombic-to-tetragonal transition exhibits a large free energy barrier and there are several competing tetragonal phases. Using large-scale molecular dynamics simulations we explore the character of these transition and observe latent heat and a discrete change in structural parameters for the tetragonal-to-cubic phase transition in both CsPbBr$_3$ and MAPbI$_3$ indicating first-order transitions. We find that in MAPbI$_3$ the orthorhombic phase has an extended metastability range and furthermore identify a second metastable tetragonal phase. Finally, we compile a phase diagram for MAPbI$_3$ that includes potential metastable phases., Comment: 11 pages, 5 figures

The development of small-angle scattering tensor tomography has enabled the study of anisotropic nanostructures in a volume-resolved manner. It is of great value to have reconstruction methods that can handle many different nanostructural symmetries. For such a method to be employed by researchers from a wide range of backgrounds, it is crucial that its reliance on prior knowledge about the system is minimized, and that it is robust under various conditions. Here, we present a method employing band-limited spherical functions to enable the reconstruction of reciprocal space maps of a wide variety of nanostructures. This method has been thoroughly tested and compared to existing methods in its ability to retrieve known reciprocal space maps, as well as its robustness to changes in initial conditions, using both simulations and experimental data. The anchoring of this method in a framework of integral geometry and linear algebra highlights its possibilities and limitations., Comment: Article has 11 pages and 6 figures, supplementary information has 7 pages and 4 figures

Halide perovskites have emerged as one of the most interesting materials for optoelectronic applications due to their favorable properties, such as defect-tolerance and long charge carrier lifetimes, which are attributed to their dynamic softness. However, this softness has led to apparent disagreements between the local instantaneous and global average structures of these materials. In this work, we assess the local tilt angles of octahedra in the perovskite structure through large-scale molecular dynamics simulations using machine learned potentials based on density functional theory. We compare structural properties given by different density functionals, namely PBE, PBEsol, SCAN, and vdW-DF-cx, and establish trends across a family of CsMX3 with M=Sn or Pb and X=Cl, Br or I perovskites. Notably, we demonstrate a strong short-range ordering that persists even in the cubic phase of halide perovskites. This ordering is reminiscent of the tetragonal phase and bridges the disordered local structure and the global cubic arrangement. Our results provide a deeper understanding of the structural properties of halide perovskites and their local distortions, which is crucial for further understanding their optoelectronic properties., Comment: 9 pages, 5 figures

It has recently been demonstrated that MoS2 with irregular interlayer rotations can achieve an extreme anisotropy in the lattice thermal conductivity (LTC), which is for example of interest for applications in waste heat management in integrated circuits. Here, we show by atomic scale simulations based on machine-learned potentials that this principle extends to other two-dimensional materials including C and BN. In all three materials introducing rotational disorder drives the through-plane LTC to the glass limit, while the in-plane LTC remains almost unchanged compared to the ideal bulk materials. We demonstrate that the ultralow through-plane LTC is connected to the collapse of their transverse acoustic modes in the through-plane direction. Furthermore, we find that the twist angle in periodic moir\'e structures representing rotational order provides an efficient means for tuning the through-plane LTC that operates for all chemistries considered here. The minimal through-plane LTC is obtained for angles between 1 and 4 degree depending on the material, with the biggest effect in MoS2. The angular dependence is correlated with the degree of stacking disorder in the materials, which in turn is connected to the slip surface. This provides a simple descriptor for predicting the optimal conditions at which the LTC is expected to become minimal., Comment: 10 pages, 6 figures

The atomic scale dynamics of halide perovskites have a direct impact not only on their thermal stability but their optoelectronic properties. Progress in machine learned potentials has only recently enabled modeling the finite temperature behavior of these material using fully atomistic methods with near first-principles accuracy. Here, we systematically analyze the impact of heating and cooling rate, simulation size, model uncertainty, and the role of the underlying exchange-correlation functional on the phase behavior of CsPbX3 with X=Cl, Br, and I, including both the perovskite and the delta-phases. We show that rates below approximately 30 K/ns and system sizes of at least a few ten thousand atoms are indicated to achieve convergence with regard to these parameters. By controlling these factors and constructing models that are specific for different exchange-correlation functionals we then show that the semi-local functionals considered in this work (SCAN, vdW-DF-cx, PBEsol, and PBE) systematically underestimate the transition temperatures separating the perovskite phases while overestimating the lattice parameters. Among the considered functionals the vdW-DF-cx functional yields the closest agreement with experiment, followed by SCAN, PBEsol, and PBE. Our work provides guidelines for the systematic analysis of dynamics and phase transitions in inorganic halide perovskites and similar systems. It also serves as a benchmark for the further development of machine-learned potentials as well as exchange-correlation functionals.

The soft modes associated with continuous-order phase transitions are associated with strong anharmonicity. This leads to the overdamped limit where the phonon quasi-particle picture can breakdown. However, this limit is commonly restricted to a narrow temperature range, making it difficult to observe its signature feature, namely the breakdown of the inverse relationship between the relaxation time and damping. Here we present a physically intuitive picture based on the relaxation times of the mode coordinate and its conjugate momentum, which at the instability approach infinity and the inverse damping factor, respectively. We demonstrate this behavior for the cubic-to-tetragonal phase transition of the inorganic halide perovskite CsPbBr$_3$ via molecular dynamics, and show that the overdamped region extends almost 200 K above the transition temperature. Further, we investigate how the dynamics of these soft phonon modes change when crossing the phase transition., Comment: 9 pages, 6 figures

While direct hot-carrier transfer can increase photo-catalytic activity, it is difficult to discern experimentally and competes with several other mechanisms. To shed light on these aspects, here, we model from first principles hot-carrier generation across the interface between plasmonic nanoparticles and a CO molecule. The hot-electron transfer probability depends non-monotonically on the nanoparticle-molecule distance and can be effective at long distances, well outside the region of chemisorption; hot-hole transfer on the other hand is limited to shorter distances. These observations can be explained by the energetic alignment between molecular and nanoparticle states as well as the excitation frequency. The hybridization of the molecular orbitals is the key predictor for hot-carrier transfer in these systems, emphasizing the need to include the effects of ground state hybridization for accurate predictions. Finally, we show a non-trivial dependence of the hot-carrier distribution on the excitation energy, which could be exploited when optimizing photo-catalytic systems., Comment: 11 pages, 5 figures

Moir\'e superlattices serve as a playground for emerging phenomena, such as localization of band states, superconductivity, and localization of excitons. These superlattices are large and are often modeled in the zero angle limit, which obscures the effect of finite twist angles. Here, by means of first-principles calculations we quantify the twist-angle dependence of the moir\'e potential in the MoS$_2$ homobilayer and identify the contributions from the constituent elements of the moir\'e potential. Furthermore, by considering the zero-angle limit configurations, we show that the moir\'e potential is rather homogeneous across the transition metal dichalcogenides (TMDs) and briefly discuss the separate effects of potential shifts and hybridization on the bilayer hybrid excitons. We find that the moir\'e potential in TMDs exhibits both an electrostatic component and a hybridization component, which are intertwined and have different relative strengths in different parts of the Brillouin zone. The electrostatic component of the moir\'e potential is a varying dipole field, which has a strong twist angle dependence. In some cases, the hybridization component can be interpreted as a tunneling rate but the interpretation is not generally applicable over the full Brillouin zone., Comment: 9 pages, 10 figures

We present our latest advancements of machine-learned potentials (MLPs) based on the neuroevolution potential (NEP) framework introduced in [Fan et al., Phys. Rev. B 104, 104309 (2021)] and their implementation in the open-source package GPUMD. We increase the accuracy of NEP models both by improving the radial functions in the atomic-environment descriptor using a linear combination of Chebyshev basis functions and by extending the angular descriptor with some four-body and five-body contributions as in the atomic cluster expansion approach. We also detail our efficient implementation of the NEP approach in graphics processing units as well as our workflow for the construction of NEP models, and we demonstrate their application in large-scale atomistic simulations. By comparing to state-of-the-art MLPs, we show that the NEP approach not only achieves above-average accuracy but also is far more computationally efficient. These results demonstrate that the GPUMD package is a promising tool for solving challenging problems requiring highly accurate, large-scale atomistic simulations. To enable the construction of MLPs using a minimal training set, we propose an active-learning scheme based on the latent space of a pre-trained NEP model. Finally, we introduce three separate Python packages, GPYUMD, CALORINE, and PYNEP, which enable the integration of GPUMD into Python workflows., Comment: 29 pages, 15 figures, code and data available

Alloying offers a way to tune many of the properties of the transition metal dichalcogenide (TMD) monolayers. While these systems in many cases have been thoroughly investigated previously, the fundamental understanding of critical temperatures, phase diagrams and band edge alignment is still incomplete. Based on first principles calculations and alloy cluster expansions we compute the phase diagrams 72 TMD monolayer alloys and classify the mixing behavior. We show that ordered phases in general are absent at room temperature but that there exists some alloys, which have a stable Janus phase at room temperature. Furthermore, for a subset of these alloys, we quantify the band edge bowing and show that the band edge positions for the mixing alloys can be continuously tuned in the range set by the boundary phases., Comment: 10 pages, 6 figures

Pd nanoalloys show great potential as hysteresis-free, reliable hydrogen sensors. Here, a multi-scale modeling approach is employed to determine optimal conditions for optical hydrogen sensing using the Pd-Au-H system. Changes in hydrogen pressure translate to changes in hydrogen content and eventually the optical spectrum. At the single particle level, the shift of the plasmon peak position with hydrogen concentration (i.e., the "optical" sensitivity) is approximately constant at 180 nm/c_H for nanodisk diameters >~ 100 nm. For smaller particles, the optical sensitivity is negative and increases with decreasing diameter, due to the emergence of a second peak originating from coupling between a localized surface plasmon and interband transitions. In addition to tracking peak position, the onset of extinction as well as extinction at fixed wavelengths is considered. We carefully compare the simulation results with experimental data and assess the potential sources for discrepancies. Invariably, the results suggest that there is an upper bound for the optical sensitivity that cannot be overcome by engineering composition and/or geometry. While the alloy composition has a limited impact on optical sensitivity, it can strongly affect H uptake and consequently the "thermodynamic" sensitivity and the detection limit. Here, it is shown how the latter can be improved by compositional engineering and even substantially enhanced via the formation of an ordered phase that can be synthesized at higher hydrogen partial pressures., Comment: 14 pages, 8 figures

The thermodynamics of phase transitions between phases that are size-mismatched but coherent differs from conventional stress-free thermodynamics. Most notably, in open systems such phase transitions are always associated with hysteresis. In spite of experimental evidence for the relevance of these effects in technologically important materials such as Pd hydride, a recipe for first-principles-based atomic-scale modeling of coherent, open systems has been lacking. Here, we develop a methodology for quantifying phase boundaries, hysteresis, and coherent interface free energies using density-functional theory, alloy cluster expansions, and Monte Carlo simulations in a constrained ensemble. We apply this approach to Pd--H and show that the phase transition changes character above approximately 400 K, occurring with an at all times spatially homogeneous hydrogen concentration, i.e., without coexistence between the two phases. Our results are consistent with experimental observations but reveal aspects of hydride formation in Pd nanoparticles that have not yet been accessible in experiment., Comment: 11 pages, 6 figures

van der Waals heterostructures consisting of vertically stacked transition-metal dichalcogenides (TMDs) exhibit a rich landscape of bright and dark intra- and interlayer excitons. In spite of a growing literature in this field of research, the type of excitons dominating optical spectra in different van der Waals heterostructures has not yet been well established. The spectral position of exciton states depends strongly on the strength of hybridization and energy renormalization due to the periodic moir\'e potential. Combining exciton density-matrix formalism and density-functional theory, we shed light on the exciton landscape in TMD homo- and heterobilayers at different stackings. This allows us to identify on a microscopic footing the energetically lowest-lying exciton state for each material and stacking. Furthermore, we disentangle the contribution of hybridization and layer polarization-induced alignment shifts of dark and bright excitons in photoluminescence spectra. By revealing the exciton landscape in van der Waals heterostructures, our work provides the basis for further studies of the optical, dynamical, and transport properties of this technologically promising class of nanomaterials., Comment: 15 pages, 9 figures

Due to their ability to reversibly absorb/desorb hydrogen without hysteresis, Pd--Au nanoalloys have been proposed as materials for hydrogen sensing. For sensing, it is important that absorption/desorption isotherms are reproducible and stable over time. A few studies have pointed to the influence of short and long range chemical order on these isotherms, but many aspects of the impact of chemical order have remained unexplored. Here, we use alloy cluster expansions to describe the thermodynamics of hydrogen in Pd--Au in a wide concentration range. We investigate how different chemical orderings, corresponding to annealing at different temperatures as well as different external pressures of hydrogen, impact the behavior of the material with focus on its hydrogen absorption/desorption isotherms. In particular, we find that a long-range ordered L1$_2$ phase is expected to form if the \ce{H2} pressure is sufficiently high. Furthermore, we construct the phase diagram at temperatures from \unit[250]{K} to \unit[500]{K}, showing that if full equilibrium is reached in the presence of hydrogen, phase separation can often be expected to occur, in stark contrast to the phase diagram in para-equilibrium. Our results explain the experimental observation that absorption/desorption isotherms in Pd--Au are often stable over time, but also reveal pitfalls for when this may not be the case., Comment: 14 pages, 8 figures

Strong light-matter interactions facilitate not only emerging applications in quantum and non-linear optics but also modifications of materials properties. In particular the latter possibility has spurred the development of advanced theoretical techniques that can accurately capture both quantum optical and quantum chemical degrees of freedom. These methods are, however, computationally very demanding, which limits their application range. Here, we demonstrate that the optical spectra of nanoparticle-molecule assemblies, including strong coupling effects, can be predicted with good accuracy using a subsystem approach, in which the response functions of the different units are coupled only at the dipolar level. We demonstrate this approach by comparison with previous time-dependent density functional theory calculations for fully coupled systems of Al nanoparticles and benzene molecules. While the present study only considers few-particle systems, the approach can be readily extended to much larger systems and to include explicit optical-cavity modes., Comment: 9 pages, 4 figures

Silicon carbide with optically and magnetically active point defects offers unique opportunities for quantum technology applications. Since interaction with these defects commonly happens through optical excitation and de-excitation, a complete understanding of their light-matter interaction in general and optical signatures, in particular, is crucial. Here, we employ quantum mechanical density functional theory calculations to investigate the photoluminescence lineshapes of selected, experimentally observed color centers (including single vacancies, double vacancies, and vacancy impurity pairs) in 4H-SiC. The analysis of zero-phonon lines as well as Huang-Rhys and Debye-Waller factors are accompanied by a detailed study of the underlying lattice vibrations. We show that the defect lineshapes are governed by strong coupling to bulk phonons at lower energies and localized vibrational modes at higher energies. Generally, good agreement to the available experimental data is obtained, and thus we expect our theoretical work to be beneficial for the identification of defect signatures in the photoluminescence spectra and thereby advance the research in quantum photonics and quantum information processing., Comment: 8 pages

Perturbative treatments of the lattice dynamics are widely successful for many crystalline materials, their applicability is, however, limited for strongly anharmonic systems, metastable crystal structures and liquids. The full dynamics of these systems can, however, be accessed via molecular dynamics (MD) simulations using correlation functions, which includes dynamical structure factors providing a direct bridge to experiment. To simplify the analysis of correlation functions, here the dynasor package is presented as a flexible and efficient tool that enables the calculation of static and dynamical structure factors, current correlation functions as well as their partial counterparts from MD trajectories. The dynasor code can handle input from several major open source MD packages and thanks to its C/Python structure can be readily extended to support additional codes. The utility of dynasor is demonstrated via examples for both solid and liquid single and multi-component systems. In particular, the possibility to extract the full temperature dependence of phonon frequencies and lifetimes is emphasized.

Color centers in h-BN are among the brightest emission centers known yet the origins of these emission centers are not well understood. Here, using first-principles calculations in combination with the generating function method, we systematically elucidate the coupling of specific defects to the vibrational degrees of freedom. We show that the lineshape of many defects exhibits strong coupling to high frequency phonon modes and that C$_{\text{N}}$, C$_{\text{B}}$, C$_{\text{B}}$-C$_{\text{N}}$ dimer and V$_{\text{B}}$ can be associated with experimental lineshapes. Our detailed theoretical study serves as a guide to identify optically active defects in h-BN that can suit specific applications in photonic-based quantum technologies, such as single photon emitters, hybrid spin-photon interfaces, or spin-mechanics interfaces., Comment: 14 pages, 9 figures

Luminescence quenching via non-radiative recombination channels limits the efficiency of optical materials such as phosphors and scintillators and therefore has implications for conversion efficiency and device lifetimes. In materials such as Ce-doped yttrium aluminum garnet (YAG:Ce), quenching shows a strong dependence on both temperature and activator concentration, limiting the ability to fabricate high-intensity white-light emitting diodes with high operating temperatures. Here, we reveal by means of first-principles calculations an efficient recombination mechanism in YAG:Ce that involves oxygen vacancies and gives rise to thermally activated concentration quenching. We demonstrate that the key requirements for this mechanism to be active are localized states with strong electron-phonon coupling. These conditions are commonly found for intrinsic defects such as anion vacancies in wide band-gap materials. The present findings are therefore relevant to a broad class of optical materials and shine light on thermal quenching mechanisms in general., Comment: 8 pages, 6 figures

Stacking monolayers of transition metal dichalcogenides into a heterostructure with a finite twist-angle gives rise to artificial moir\'e superlattices with a tunable periodicity. As a consequence, excitons experience a periodic potential, which can be exploited to tailor optoelectronic properties of these materials. While recent experimental studies have confirmed twist-angle dependent optical spectra, the microscopic origin of moir\'e exciton resonances has not been fully clarified yet. Here, we combine first principle calculations with the excitonic density matrix formalism to study transitions between different moir\'e exciton phases and their impact on optical properties of the twisted MoSe$_2$/WSe$_2$ heterostructure. At angles smaller than 2$^{\circ}$ we find flat, moir\'e trapped states for inter- and intralayer excitons. This moir\'e exciton phase drastically changes into completely delocalized states already at 3$^{\circ}$. We predict a linear and quadratic twist-angle dependence of excitonic resonances for the moir\'e-trapped and delocalized exciton phase, respectively. Our work provides microscopic insights opening the possibility to tailor moir\'e exciton phases in van der Waals superlattices.

We report a comprehensive study of aqueous halide adsorption on nanoparticles of gold and palladium that addresses several limitations hampering the use of atomistic modeling as a tool for understanding and improving wet-chemical synthesis and related applications. A combination of thermodynamic modeling with density functional theory (DFT) calculations and experimental data is used to predict equilibrium shapes of halide-covered nanoparticles as a function of the chemical environment. To ensure realistic and experimentally relevant results, we account for solvent effects and include a large set of vicinal surfaces, several adsorbate coverages as well as decahedral particles. While the observed stabilization is not significant enough to result in thermodynamic stability of anisotropic shapes such as nanocubes, non-uniformity in the halide coverage indicates the possibility of obtaining such shapes as kinetic products. With regard to technical challenges, we show that inclusion of surface-solvent interactions lead to qualitative changes in the predicted shape. Furthermore, accounting for non-local interactions on the functional level yields a more accurate description of surface systems., Comment: 10 pages, 7 figures, 1 table

Metal nanoparticles are attractive for plasmon-enhanced generation of hot carriers, which may be harnessed in photochemical reactions. In this work, we analyze the coherent femtosecond dynamics of photon absorption, plasmon formation, and subsequent hot-carrier generation through plasmon dephasing using first-principles simulations. We predict the energetic and spatial hot-carrier distributions in small metal nanoparticles and show that the distribution of hot electrons is very sensitive to the local structure. Our results show that surface sites exhibit enhanced hot-electron generation in comparison to the bulk of the nanoparticle. While the details of the distribution depend on particle size and shape, as a general trend lower-coordinated surface sites such as corners, edges, and {100} facets exhibit a higher proportion of hot electrons than higher-coordinated surface sites such as {111} facets or the core sites. The present results thereby demonstrate how hot carriers could be tailored by careful design of atomic-scale structures in nanoscale systems., Comment: 10 pages, 4 figures

Point defects play a crucial role in crystalline materials as they do not only impact the thermodynamic properties but are also central to kinetic processes. While they are necessary in thermodynamic equilibrium spontaneous defect formation in the bulk is normally considered highly improbable except for temperatures close to the melting point. Here, we demonstrate by means of atomistic simulations that processes involving concerted atomic motion that give rise to defect formation are in fact frequent in body-centered cubic metals even down to about 50% of the melting temperature. It is shown that this behavior is intimately related to the anharmonicity of the lattice vibrations and a flat energy landscape along certain crystallographic directions, a feature that is absent in, e.g., face-centered cubic lattice structures. This insight has implications for our general understanding of these materials and furthermore provides a complementary explanation for the so-called anomalous diffusion in group 4 transition metals., Comment: 5 pages; 4 figures

Using a first-principles approach, we analyze the impact of \textit{DX} centers formed by S, Se, and Te dopant atoms on the thermal conductivity of GaAs. Our results are in good agreement with experiments and unveil the physics behind the drastically different effect of each kind of defect. We establish a causal chain linking the electronic structure of the dopants to the thermal conductivity of the bulk solid, a macroscopic transport coefficient. Specifically, the presence of lone pairs leads to the formation of structurally asymmetric \textit{DX} centers that cause resonant scattering of incident phonons. The effect of such resonances is magnified when they affect the part of the spectrum most relevant for thermal transport. We show that these resonances are associated with localized vibrational modes in the perturbed phonon spectrum. Finally, we illustrate the connection between flat adjacent minima in the energy landscape and resonant phonon scattering through detailed analyses of the energy landscape of the defective structures., Comment: 7 pages, 7 figures

Phase diagrams for multi-component systems represent crucial information for understanding and designing materials but are very time consuming to assess experimentally. Computational modeling plays an increasingly important role in this regard but has been largely focused on systems with matching lattice structures and/or stable boundary phases. Here, using a combination of density functional theory calculations, alloy cluster expansions, free energy integration, and Monte Carlo simulations, we obtain the phase diagram of W-Ti, a system that features metastable boundary phases on both sides of the phase diagram. We find that the mixing energy on the body-centered cubic (BCC) lattice is asymmetric and negative with a minimum of about -120 meV/atom, whereas for the hexagonal closed packed (HCP) lattice the mixing energy is positive. By combining these data with a model for the vibrational free energy, we propose a revision of the W-rich end of the phase diagram with a much larger solubility of Ti in BCC-W than previous assessments. Finally, by comparison with the W-V and W-Re system we demonstrate how strongly asymmetric phase diagrams can arise from a subtle energy balance of stable and metastable lattice structures., Comment: 9 pages, 7 figures

Strong light-matter interactions in both the single-emitter and collective strong coupling regimes attract significant attention due to emerging quantum and nonlinear optics applications, as well as opportunities for modifying material-related properties. Further exploration of these phenomena requires an appropriate theoretical methodology, which is demanding since polaritons are at the intersection between quantum optics, solid state physics and quantum chemistry. Fortunately, however, nanoscale polaritons can be realized in small plasmon-molecule systems, which in principle allows treating them using ab initio methods, although this has not been demonstrated to date. Here, we show that time-dependent density-functional theory (TDDFT) calculations can access the physics of nanoscale plasmon-molecule hybrids and predict vacuum Rabi splitting in a system comprising a few-hundred-atom aluminum nanoparticle interacting with one or several benzene molecules. We show that the cavity quantum electrodynamics approach holds down to resonators on the order of a few cubic nanometers, yielding a single-molecule coupling strength exceeding 200 meV due to a massive vacuum field value of 4.5 V/nm. In a broader perspective, our approach enables parameter-free in-depth studies of polaritonic systems, including ground state, chemical and thermodynamic modifications of the molecules in the strong-coupling regime, which may find important use in emerging applications such as cavity enhanced catalysis., Comment: 6 pages, 5 figures

Linear models, such as force constant (FC) and cluster expansions, play a key role in physics and materials science. While they can in principle be parametrized using regression and feature selection approaches, the convergence behavior of these techniques, in particular with respect to thermodynamic properties is not well understood. Here, we therefore analyze the efficacy and efficiency of several state-of-the-art regression and feature selection methods, in particular in the context of FC extraction and the prediction of different thermodynamic properties. Generic feature selection algorithms such as recursive feature elimination with ordinary least-squares (OLS), automatic relevance determination regression, and the adaptive least absolute shrinkage and selection operator can yield physically sound models for systems with a modest number of degrees of freedom. For large unit cells with low symmetry and/or high-order expansions they come, however, with a non-negligible computational cost that can be more than two orders of magnitude higher than that of OLS. In such cases, OLS with cutoff selection provides a viable route as demonstrated here for both second-order FCs in large low-symmetry unit cells and high-order FCs in low-symmetry systems. While regression techniques are thus very powerful, they require well-tuned protocols. Here, the present work establishes guidelines for the design of protocols that are readily usable, e.g., in high-throughput and materials discovery schemes. Since the underlying algorithms are not specific to FC construction, the general conclusions drawn here also have a bearing on the construction of other linear models in physics and materials science., Comment: 15 pages, 12 figures

Alloy cluster expansions (CEs) provide an accurate and computationally efficient mapping of the potential energy surface of multi-component systems that enables comprehensive sampling of the many-dimensional configuration space. Here, we introduce \textsc{icet}, a flexible, extensible, and computationally efficient software package for the construction and sampling of CEs. \textsc{icet} is largely written in Python for easy integration in comprehensive workflows, including first-principles calculations for the generation of reference data and machine learning libraries for training and validation. The package enables training using a variety of linear regression algorithms with and without regularization, Bayesian regression, feature selection, and cross-validation. It also provides complementary functionality for structure enumeration and mapping as well as data management and analysis. Potential applications are illustrated by two examples, including the computation of the phase diagram of a prototypical metallic alloy and the analysis of chemical ordering in an inorganic semiconductor., Comment: 10 pages

The efficient extraction of force constants (FCs) is crucial for the analysis of many thermodynamic materials properties. Approaches based on the systematic enumeration of finite differences scale poorly with system size and can rarely extend beyond third order when input data is obtained from first-principles calculations. Methods based on parameter fitting in the spirit of interatomic potentials, on the other hand, can extract FC parameters from semi-random configurations of high information density and advanced regularized regression methods can recover physical solutions from a limited amount of data. Here, we present the hiPhive Python package, that enables the construction of force constant models up to arbitrary order. hiPhive exploits crystal symmetries to reduce the number of free parameters and then employs advanced machine learning algorithms to extract the force constants. Depending on the problem at hand both over and underdetermined systems are handled efficiently. The FCs can be subsequently analyzed directly and or be used to carry out e.g., molecular dynamics simulations. The utility of this approach is demonstrated via several examples including ideal and defective monolayers of MoS$_2$ as well as bulk nickel.