arXiv subjects

Paul Erhart

Publications and source records attributed to Paul Erhart.

At least 37 records · Page 2Linked to original sources

arXiv2025

Revealing the Low Temperature Phase of FAPbI$_3$ using A Machine-Learned Potential

FAPbI$_3$ is a material of interest for its potential in solar cell applications, driven by its remarkable optoelectronic properties. However, the low-temperature phase of FAPbI$_3$ remains poorly understood, with open questions surrounding its crystal structure, octahedral tilting, and the arrangement of formamidinium (FA) cations. Using our trained machine-learned potential in combination with large-scale molecular dynamics simulations, we provide a detailed investigation of this phase, uncovering its structural characteristics and dynamical behavior. Our analysis reveals the octahedral tilt pattern and sheds light on the rotational dynamics of FA cations in the low temperature phase. Strikingly, we find that the FA cations become frozen in a metastable configuration, unable to reach the thermodynamic ground state. By comparing our simulated results with experimental nuclear magnetic resonance (NMR) and inelastic neutron scattering (INS) spectra, we demonstrate good agreement, further validating our findings. This phenomenon mirrors experimental observations and offers a compelling explanation for the experimental challenges in accessing the true ground state. These findings provide critical insights into the fundamental physics of FAPbI$_3$ and its low-temperature behavior, advancing our understanding of this technologically important material.

cond-mat.mtrl-sci↗

arXiv2025

A Morphotropic Phase Boundary in MA$_{1-x}$FA$_x$PbI$_3$: Linking Structure, Dynamics, and Electronic Properties

Understanding the phase behavior of mixed-cation halide perovskites is critical for optimizing their structural stability and optoelectronic performance. Here, we map the phase diagram of MA$_{1-x}$FA$_x$PbI$_3$ using a machine-learned interatomic potential in molecular dynamics simulations. We identify a morphotropic phase boundary (MPB) at approximately 27% FA content, delineating the transition between out-of-phase and in-phase octahedral tilt patterns. Phonon mode projections reveal that this transition coincides with a mode crossover composition, where the free energy landscapes of the M and R phonon modes become nearly degenerate. This results in nanoscale layered structures with alternating tilt patterns, suggesting minimal interface energy between competing phases. Our results provide a systematic and consistent description of this important system, complementing earlier partial and sometimes conflicting experimental assessments. Furthermore, density functional theory calculations show that band edge fluctuations peak near the MPB, indicating an enhancement of electron-phonon coupling and dynamic disorder effects. These findings establish a direct link between phonon dynamics, phase behavior, and electronic structure, providing a further composition-driven pathway for tailoring the optoelectronic properties of perovskite materials. By demonstrating that phonon overdamping serves as a hallmark of the MPB, our study offers new insights into the design principles for stable, high-performance perovskite solar cells.

cond-mat.mtrl-sci↗

arXiv2025

Dynasor 2: From Simulation to Experiment Through Correlation Functions

Correlation functions, such as static and dynamic structure factors, offer a versatile approach to analyzing atomic-scale structure and dynamics. By having access to the full dynamics from atomistic simulations, they serve as valuable tools for understanding material behavior. Experimentally, material properties are commonly probed through scattering measurements, which also provide access to static and dynamic structure factors. However, it is not trivial to decode these due to complex interactions between atomic motion and the probe. Atomistic simulations can help bridge this gap, allowing for detailed understanding of the underlying dynamics. In this paper, we illustrate how correlation functions provide structural and dynamical insights from simulation and showcase the strong agreement with experiment. To compute the correlation functions, we have updated the Python package dynasor with a new interface and, importantly, added support for weighting the computed quantities with form factors or cross sections, facilitating direct comparison with probe-specific structure factors. Additionally, we have incorporated the spectral energy density method, which offers an alternative view of the dispersion for crystalline systems, as well as functionality to project atomic dynamics onto phonon modes, enabling detailed analysis of specific phonon modes from atomistic simulation. We illustrate the capabilities of dynasor with diverse examples, ranging from liquid Ni3Al to perovskites, and compare computed results with X-ray, electron and neutron scattering experiments. This highlights how computed correlation functions can not only agree well with experimental observations, but also provide deeper insight into the atomic-scale structure and dynamics of a material.

cond-mat.mtrl-sci↗

arXiv2025

Octahedral tilt-driven phase transitions in BaZrS$_3$ chalcogenide perovskite

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 BaZrS$_3$ remain underexplored in the literature, as most experimental characterizations of this material have been 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-learning interatomic potential trained with data from hybrid density functional theory calculations. At 0 Pa, we find a first-order phase transition from the orthorhombic to tetragonal I4/mcm phase at 610 K, and a second-order transition from the tetragonal to the cubic Pm-3m phase at 880 K. The tetragonal phase is stable over a larger temperature range at higher pressures. To confirm the validity of our model we compare our results with a range of published experimental data and report a prediction for the X-ray diffraction pattern as a function of temperature.

cond-mat.mtrl-sci↗

arXiv2025

Probing Glass Formation in Perylene Derivatives via Atomic Scale Simulations and Bayesian Regression

While the structural dynamics of chromophores are of interest for a range of applications, it is experimentally very challenging to resolve the underlying microscopic mechanisms. Glassy dynamics are also challenging for atomistic simulations due to the underlying dramatic slowdown over many orders of magnitude. Here, we address this issue by combining atomic scale simulations with autocorrelation function analysis and Bayesian regression, and apply this approach to a set of perylene derivatives as prototypical chromophores. The predicted glass transition temperatures and kinetic fragilities are in semi-quantitative agreement with experimental data. By analyzing the underlying dynamics via the normal vector autocorrelation function, we are able to connect the beta and alpha-relaxation processes in these materials to caged (or librational) dynamics and cooperative rotations of the molecules, respectively. The workflow presented in this work serves as a stepping stone toward understanding glassy dynamics in many-component mixtures of perylene derivatives and is readily extendable to other systems of chromophores.

cond-mat.mtrl-sci↗

arXiv2024

Untangling the Raman spectrum of cubic and tetragonal BaZrO$_3$

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.

cond-mat.mtrl-sci↗

arXiv2024

Highly efficient path-integral molecular dynamics simulations with GPUMD using neuroevolution potentials: Case studies on thermal properties of materials

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.

cond-mat.mtrl-sci↗

arXiv2024

Construction and sampling of alloy cluster expansions -- A tutorial

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.

cond-mat.mtrl-sci↗

arXiv2024

Direct, indirect, and self-trapped excitons in Cs$_2$AgBiBr$_6$

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($α$) 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.

cond-mat.mtrl-sci↗

arXiv2024

Optical line shapes of color centers in solids from classical autocorrelation functions

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.

cond-mat.mtrl-sci↗

arXiv2024

Controlling Plasmonic Catalysis via Strong Coupling with Electromagnetic Resonators

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.

cond-mat.mtrl-sci↗

arXiv2024

General-purpose machine-learned potential for 16 elemental metals and their alloys

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.

cond-mat.mtrl-sci↗

arXiv2024

Defect-assisted reversible phase transition in mono- and few-layer ReS$_2$

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.

physics.optics↗

arXiv2024

Impact of Surface Adsorbates and Dimensionality on Templating of Halide Perovskites

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.

cond-mat.mtrl-sci↗

arXiv2024

Investigating the missing wedge problem in small-angle x-ray scattering tensor tomography across real and reciprocal space

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.

physics.med-ph↗

arXiv2024

GPAW: An open Python package for electronic-structure calculations

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.

cond-mat.mtrl-sci↗

arXiv2024

Tensorial properties via the neuroevolution potential framework: Fast simulation of infrared and Raman spectra

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.

cond-mat.mes-hall↗

arXiv2024

Machine Learning for Polaritonic Chemistry: Accessing chemical kinetics

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.

physics.chem-ph↗