arXiv subjects

Paul Erhart

Publications and source records attributed to Paul Erhart.

At least 19 recordsLinked to original sources

arXiv2026

Strong Impact of Halide Ordering on Structural Phase Transitions in Mixed Perovskites

Mixed halide perovskites are highly versatile semiconductors with applications in photovoltaics, light-emitting diodes, and photodetectors. Understanding their thermodynamic phase behavior is central to guiding compositional design and improving device stability. Here, we train machine-learned interatomic potentials (MLIPs) on density functional theory reference data for CsxRb1-xPbBr3yI3-3y, CsxRb1-xPbBr3yCl3-3y, and CsxRb1-xPbCl3yI3-3y halide perovskites, enabling large-scale hybrid Monte Carlo-molecular dynamics simulations that sample both configurational and vibrational degrees of freedom. All three binary halide systems exhibit a miscibility gap, the extent of which correlates with halide ion size mismatch. The gaps in Br-Cl and Br-I close at low temperatures, while the Cl-I gap extends above room temperature. At temperatures above the miscibility gap (200 K to 500 K), all systems show a tendency toward layered halide ordering, with halide species preferentially occupying apical or equatorial octahedral sites. In CsPbBr3yI3-3y, this ordering occurs in a device-relevant temperature regime and is linked to the structural phase transitions, shifting transition temperatures by up to 100 K relative to randomly mixed structures. We attribute the strongly non-linear composition dependence of the orthorhombic-tetragonal phase boundary observed experimentally (a linear decrease followed by a plateau) to halide ordering. Introducing Rb on the A-site weakens halide ordering and eliminates the non-linear behavior, while narrowing the miscibility gap in both the Br-I and Br-Cl systems. These results establish halide ordering as a key determinant of structural phase stability in mixed-halide perovskites.

cond-mat.mtrl-sci↗

arXiv2026

Interplay of B-Site Off-Centering and Molecular Orientations in the Mixed Hybrid Perovskite MAGe1xSnxI3

B-site mixing is a common strategy for tuning properties of halide perovskites. In the lead-free system MAGe1-xSn_xI3, it brings tilting and off-centering into competition. Using large-scale molecular dynamics driven by a machine-learned interatomic potential, we map the structural behavior across the full composition range. MAGeI3 exhibits strong polar B-site off-centering that remains nearly constant up to the cubic transition, together with methylammonium (MA) orientational order that weakens progressively on heating. By contrast, MASnI3 combines octahedral tilting with weaker, predominantly antipolar off-centering. Ge-like behavior persists upon alloying and gives way to Sn-like behavior only beyond roughly 65 % Sn. In the high-temperature phases, the B-site cations remain locally off-centered but directionally disordered. On the Ge-rich side, the distorted inorganic framework biases the soft MA orientational landscape toward a restricted set of preferred directions. This coupling shows how the composition of the inorganic sublattice can tune molecular ordering in lead-free hybrid perovskites.

cond-mat.mtrl-sci↗

arXiv2026

Thermal history controls the optoelectronic response of lead halide perovskites through structure and dynamics

Lead halide perovskites are promising optoelectronic materials for photovoltaics, light emission and detection. Their efficiencies in PV now approach the detailed-balance limit, leaving stability as the principal barrier. The intrinsic instabilities studied to date centre on ionic motion within a fixed, homogeneous lattice. Here we identify a further source of intrinsic structural instability, hidden in the lattice dynamics. Mapping caesium, methylammonium and formamidinium-based compositions with Cl, Br, I and mixed X-sites through all accessible phases, using single crystal X-ray and neutron diffuse scattering, machine-learning-assisted molecular dynamics, a phenomenological octahedral tilt model and hyperspectral photoluminescence, we find that nearly every composition hosts equilibrium local structural fluctuations: dynamic nanodomains of correlated octahedral tilts, a few nanometres in size, that locally break the crystallographic symmetry. Three complementary levers control them. The A-site cation sets their symmetry, shape and anisotropy, from sparse, isotropic and tetragonal in formamidinium-based compositions to dense, anisotropic and orthorhombic in nominally cubic caesium-based ones, the most locally disordered we studied. The halide controls the dynamic disorder and the phase-transition sequence. Thermal history is the third: different ramp rates drive nominally identical compositions into distinct crystallographic phases, each with its own hidden local order. In MAPbI3, the heating rate alone changes the photoluminescence quantum efficiency across the phase transition. Because these transitions lie within device operating ranges, from terrestrial thermal cycling to the extremes of space, thermal history may shape the local structure, and hence the optoelectronic response, throughout fabrication and operation, establishing it as a design variable alongside composition.

cond-mat.mtrl-sci↗

arXiv2026

Bayesian Optimization for Practical H2 Sensors: Inverse Design of Pd-based Plasmonic Metasurfaces

Hydrogen detection is becoming increasingly important as its use grows across energy and industrial systems. Optical sensing platforms based on palladium (Pd) nanoparticles are attractive for this task because hydrogen uptake directly alters their plasmonic response. Organizing such nanoparticles into periodic two-dimensional arrays, known as metasurfaces, further enhances their optical response through collective resonances. However, the large design space presented by chemical composition, nanoparticle geometry, and array structure calls for systematic approaches for optimizing complex nanoalloy metasurface geometries. Here, we develop an inverse-design framework based on Bayesian optimization that couples first-principles dielectric functions with electromagnetic simulations to identify high-performance PdAu nanodisk arrays for hydrogen sensing in the 1 to 100 mbar range where the flammability of H2 becomes a concern. We use our approach to search a five-dimensional design space, comprising nanodisk height and radius, array pitch, polymer coating thickness, and Au fraction in order to maximize the H-induced change in extinction at a single wavelength of choice. The results show that integrating first-principles optical models with data-efficient optimization yields experimentally feasible nanoparticle metasurfaces tailored for targeted hydrogen pressures, while providing a pathway to future multiobjective sensor design. They also reveal remaining gaps in the modeling methodologies that still limit the quantitative reliability of the approach.

physics.optics↗

arXiv2026

Octahedral Tilting in Halide Double Perovskites: Disentangling Lone-Pair Chemistry and Geometric Effects

Halide double perovskites (HDPs) have emerged as promising alternatives to their lead-based counterparts. However, their structural dynamics is less explored than that of conventional halide perovskites. In this work, we investigate octahedral tilting at 0 K and the relative stability of tetragonal and cubic phases of a set of 57 halide double perovskites (HDPs). By combining structural and energetic descriptors with simple geometric metrics, we identify the main trends controlling the stabilization of one-tilt tetragonal phases across this family. We find that both the magnitude of the tilt angles and the energetic preference for tilted phases correlate primarily with the Goldschmidt tolerance factor $t$. The presence of stereochemically active lone-pair cations also correlates with enhanced tilting; however, this trend largely reflects that lone-pair chemistries in HDPs occur together with ionic sizes that shift $t$ away from unity. Consistent with this picture, we observe several compounds without lone pairs that nonetheless exhibit strong octahedral tilting. Finally, using machine-learned interatomic potentials, we connect the 0 K tilting energetics to finite-temperature behavior: compounds with more strongly stabilized tilt phases exhibit higher transition temperatures, and phonon spectra at 350 K reveal soft and broad modes that are consistent with the trends in tolerance factors, tilt angles, and tilt energies at 0 K. Our results provide a systematic reference for structure-stability relationships in HDPs and clarify when lone-pair chemistry is correlated with, rather than the primary cause of, octahedral tilting.

cond-mat.mtrl-sci↗

arXiv2026

Efficient method for calculation of low-temperature phase boundaries

Understanding phase stability and phase transformations is central to predicting material behavior under varying thermodynamic conditions. One of the earliest and most influential applications of density functional theory in materials science has been the prediction of pressure-induced phase transitions at 0 K. Extending these calculations to finite temperatures, however, requires accounting for thermal, quantum, and anharmonic contributions to the free energy, often at significant computational cost. In this work, we present a general and efficient framework for calculating low-temperature phase boundaries by combining the Clausius-Clapeyron equation with the quasi-harmonic approximation. This methodology requires a minimal number of calculations, while naturally incorporating internal degrees of freedom as well as quantum and low-order anharmonic effects. We illustrate the accuracy and efficiency of the approach by constructing the phase diagram of silica in the pressure range from -2 to 12 GPa and temperatures up to 1750 K. To this end, we employ a machine-learned interatomic potential trained on density functional theory reference data, enabling well-converged free energy estimates via efficient thermodynamic sampling and a rigorous comparison between the proposed framework and free energy integration.

cond-mat.mtrl-sci↗

arXiv2026

Revealing the kinetics of interfacial surfactant phase transitions through multiscale simulations and in-situ plasmonic sensing

Surfactant self-assembly at solid-liquid interfaces governs interfacial stability, transport, and reactivity across many technologies, yet resolving interfacial surfactant phases and their transition kinetics in situ remains challenging. Here, we establish an atomistically grounded plasmonic framework that quantitatively maps interfacial surfactant phases and phase transitions onto optical signatures. Distinct morphologies differ in packing and hydration, modifying the effective permittivity within the optical near field and producing surfactant phase-specific plasmonic extinction peak shifts. Using cetyltrimethylammonium bromide on silica as a prototypical surfactant-surface system, we combine atomistic simulations, electronic-structure calculations, and continuum electrodynamics to translate molecular morphologies into predicted spectral shifts for literature-reported surface phases. We experimentally confirm the predicted ordering and magnitude of steady-state peak shifts during stepwise concentration changes, and extract transition kinetics from exponential relaxations of the time-resolved peak shift. A key mechanistic signature is reversal of the spectral shift direction upon transition from an impermeable bilayer to a water-accessible, channel-containing phase, consistent with hydration-driven reduction of the local effective permittivity. Because the approach relies on dielectric contrast in the plasmonic near field and works through a dielectric overlayer, it provides a broadly applicable route for real-time identification of interfacial surfactant phases and their kinetics in aqueous conditions.

cond-mat.mtrl-sci↗

arXiv2026

Anion Ordering and Phase Stability Govern Optical Band Gaps in BaZr(S,Se)3

Chalcogenide perovskites have emerged as promising lead free materials for photovoltaic and thermoelectric applications. Among them, BaZrS3 has attracted particular attention due to its thermal and chemical stability, favorable optoelectronic properties, and low thermal conductivity. Here, we combine molecular dynamics and Monte Carlo simulations based on machine learned interatomic potentials with scanning transmission electron microscopy to investigate mixing thermodynamics and phase stability in the BaZr(S,Se)3 system. We identify an unusual ordered structure that persists at room temperature, most prominently at 33% S, where S and Se atoms form alternating layers within the crystal. Free energy calculations yield the temperature composition phase diagram, including a nonperovskite delta phase in the Se rich limit and a perovskite phase in the S rich limit, separated by a broad two phase region. Analysis of the dielectric function and the absorption coefficient demonstrates that composition, crystal structure, and anion ordering jointly control the optical band gap. Selenium alloying enables tuning between approximately 1.6 and 1.9eV, while anion ordering within a given composition reduces the gap by about 0.12eV. Lastly, variations between structural polymorphs give rise to band gap differences of up to 0.4eV.

cond-mat.mtrl-sci↗

arXiv2026

Non-Markovian heat production in ultrafast phonon dynamics

High-intensity THz laser pulses enable the light-mediated control of lattice vibrations by resonantly driving selected phonon modes. On ultrafast timescales, memory effects influence the phonon dynamics and must be accounted for to describe the heat production associated with energy dissipation. Here, we establish a microscopic framework for non-Markovian phonon dynamics by deriving the noise and dissipation kernels governing a driven phonon mode. Using large-scale molecular dynamics simulations, we reconstruct these kernels directly from the many-body lattice dynamics and determine the corresponding heat production rate. Our results provide a quantitative picture of the crossover between Markovian and non-Markovian dynamics on picosecond timescales and show how the finite bandwidth of the driving field limits the dynamically relevant bath spectrum. Furthermore, we demonstrate that thermodynamic quantities such as heat production can be inferred directly from the dynamics of an individual phonon mode, enabling their experimental measurement using time-resolved spectroscopy.

cond-mat.mtrl-sci↗

arXiv2026

Competing adsorption of H and CO on Pd-alloy surfaces: Mechanistic insight into the mitigating effect of Cu on CO poisoning

Multi-component alloys offer broad tunability for addressing challenges in materials science, but their vast configurational space makes their surface chemistry highly sensitive to operating conditions, for example through adsorption and segregation. Here, we study Pd-Au-Cu alloy surfaces in H$_2$ and CO environments motivated by their use in H technologies, in particular plasmonic H$_2$ sensing, where alloying can mitigate limitations intrinsic to Pd such as hysteresis and CO poisoning. Modeling multicomponent surfaces with multiple adsorbate species under realistic conditions is challenging. To this end, we establish an accurate and efficient framework that combines machine-learned interatomic potentials trained on density functional theory data to generate training data for cluster expansions with effectively no limitations on training set size. By constructing continuous surface phase diagrams for H-CO coadsorption we find that coadsorption under operating conditions is governed primarily by the H coverage during annealing. Au-rich surfaces, formed under H-poor conditions, suppress both CO and H adsorption, while H-rich conditions yield Pd-rich surfaces that maintain higher H coverages compared to Pd at relevant CO partial pressures, indicating improved CO poisoning resistance. This effect is insensitive to relative amounts of Au and Cu, despite experimental evidence of the mitigating effect of specifically Cu on CO poisoning. Kinetic barriers for dilute alloy surfaces indicate that absorption pathways near Au are highly unfavorable, while Cu leave the energetics unchanged compared to pure Pd. This finding suggests that Cu in the surface region provides viable pathways to shuttle H into the material when Pd-dominated paths are blocked by CO.

cond-mat.mtrl-sci↗

arXiv2026

Diverse polymorphism in Ruddlesden-Popper chalcogenides

Ruddlesden-Popper (RP) chalcogenides are stable, non-toxic candidates for optoelectronic or thermoelectric applications. The structural diversity of RP oxides is already exploited to tune properties or achieve more advanced functionalities like multiferroicity, however, little is known about the structural evolution of RP chalcogenides. In this work, we develop a high-accuracy machine-learned interatomic potential to run large-scale molecular dynamics simulations on $Ba_{n+1}Zr_nS_{3n+1}$ for $n=1$ to $n=6$. We predict new polymorphs for each $n$-value, calculate their corresponding phase transition temperatures, and validate our approach through comparison to published experimental results. We find that the $n=1$ phase exhibits negative thermal expansion, that $n=1$ and $n=3$ undergo unusual ascending symmetry breaking, and that phases with $n\geq4$ form layer-dependent tilt patterns previously unreported for inorganic RP materials. This unique behaviour results from competition between octahedral rotations and rumpling at the rocksalt interface, and suggests new strategies for accessing advanced functionalities.

cond-mat.mtrl-sci↗

arXiv2026

qNEP: A highly efficient neuroevolution potential with dynamic charges for large-scale atomistic simulations

Although electrostatics can be incorporated into machine-learned interatomic potentials, existing approaches are computationally very demanding, limiting large-scale, long-time simulations of electrostatics-driven phenomena such as dielectric response, infrared activity, and field-matter coupling. Here, we extend the neuroevolution potential (NEP), a highly efficient machine-learned interatomic potential, to a charge-aware framework (qNEP) by introducing explicit, environment-dependent partial charges. Each ionic partial charge is represented by a neural network as a function of the local descriptor vector, analogous to the NEP site-energy model. This formulation enables the direct prediction of the Born effective charge tensor for each ion and, consequently, the polarization. As a result, dielectric properties, infrared spectra, and coupling to external electric fields can be evaluated within a unified framework. We derive consistent expressions for the forces and virials that explicitly account for the position dependence of the partial charges. The qNEP method has been implemented in the free-and-open-source GPUMD package, with support for both Ewald summation and particle-particle particle-mesh treatments of electrostatics. We demonstrate the accuracy and efficiency of the qNEP approach through representative applications to water, Li7La3Zr2O12, BaTiO3, and a magnesium-water interface. These results show that qNEP enables accurate atomistic simulations with explicit long-range electrostatics, scalable to million-atom systems on nanosecond time scales using consumer-grade GPUs.

physics.comp-ph↗

arXiv2025

Thermal Stabilization of Defect Charge States and Finite-Temperature Charge Transition Levels

Point defects introduce localized electronic states that critically affect carrier trapping, recombination, and transport in functional materials. The associated charge transition levels (CTLs) can depend on temperature, requiring accurate treatment of vibrational and electronic free-energy contributions. In this work, we use machine-learned interatomic potentials to efficiently compute temperature-dependent CTLs for vacancies in MgO, LiF, and CsSnBr3. Using thermodynamic integration, we quantify free-energy differences between charge states and calculate the vibrational entropy contributions at finite temperatures. We find that CTLs shift with temperature in MgO, LiF and CsSnBr3 from both entropy and electronic contributions. Notably, in CsSnBr3 a neutral charge state becomes thermodynamically stable above 60 K, introducing a temperature-dependent Fermi-level window absent at 0 K. We show that the widely used static, zero-kelvin defect formalism can miss both quantitative CTL shifts and the qualitative emergence of new stable charge states.

cond-mat.mtrl-sci↗

arXiv2025

rhodent: A Python package for analyzing real-time TDDFT response

Real-time time-dependent density functional theory (rt-TDDFT) is a well-established method for studying the dynamic response of matter in the femtosecond or optical range. In this method, the Kohn-Sham (KS) wave functions are propagated forward in time, and in principle, one can extract any observable at any given time. Alternatively, by taking a Fourier transform, spectroscopic quantities can be extracted. There are many publicly available codes implementing rt-TDDFT, which differ in their numeric solution of the KS equations, their available exchange-correlation functionals, and in their analysis capabilities. For users of rt-TDDFT, this is an inconvenient situation because they may need to use a numerical method that is available in one code, but an analysis method available in another. Here, we introduce rhodent, a modular Python package for processing the output of rt-TDDFT calculations. Our package can be used to calculate hot-carrier distributions, energies, induced densities, and dipole moments, and various decompositions thereof. In its current version, rhodent handles calculation results from the gpaw code, but can readily be extended to support other rt-TDDFT codes. Additionally, under the assumption of linear response, rhodent can be used to calculate the response to a narrow-band laser, from the response to a broad-band perturbation, greatly speeding up the analysis of frequency-dependent excitations. We demonstrate the capabilities of rhodent via a set of examples, for systems consisting of Al and Ag clusters and organic molecules.

physics.comp-ph↗

arXiv2025

NEP89: Universal neuroevolution potential for inorganic and organic materials across 89 elements

While machine-learned interatomic potentials offer near-quantum-mechanical accuracy for atomistic simulations, many are material-specific or computationally intensive, limiting their broader use. Here, we introduce NEP89, a foundation model based on neuroevolution potential architecture, delivering near-empirical-potential speed and high accuracy across 89 elements. A compact yet comprehensive training dataset covering inorganic and organic materials was curated through descriptor-space subsampling and iterative refinement across multiple datasets. NEP89 achieves competitive accuracy compared to representative foundation models while being three to four orders of magnitude more computationally efficient, enabling previously impractical large-scale atomistic simulations of inorganic and organic systems. In addition to its out-of-the-box applicability to diverse scenarios, including million-atom-scale compression of compositionally complex alloys, ion diffusion in solid-state electrolytes and water, rocksalt dissolution, methane combustion, and protein-ligand dynamics, NEP89 also supports fine-tuning for rapid adaptation to user-specific applications, such as mechanical, thermal, structural, and spectral properties of two-dimensional materials, metallic glasses, and organic crystals.

cond-mat.mtrl-sci↗

arXiv2025

Predicting neutron experiments from first principles: A workflow powered by machine learning

Machine learning has emerged as a powerful tool in materials discovery, enabling the rapid design of novel materials with tailored properties for countless applications, including in the context of energy and sustainability. To ensure the reliability of these methods, however, rigorous validation against experimental data is essential. Scattering techniques -- using neutrons, X-rays, or electrons -- offer a direct way to probe atomic-scale structure and dynamics, making them ideal for this purpose. In this work, we describe a computational workflow that bridges machine learning-based simulations with experimental validation. The workflow combines density functional theory, machine-learned interatomic potentials, molecular dynamics, and autocorrelation function analysis to simulate experimental signatures, with a focus on inelastic neutron scattering. We demonstrate the approach on three representative systems: crystalline silicon, crystalline benzene, and hydrogenated scandium-doped BaTiO3, comparing the simulated spectra to measurements from four different neutron spectrometers. While our primary focus is inelastic neutron scattering, the workflow is readily extendable to other modalities, including diffraction and quasi-elastic scattering of neutrons, X-rays, and electrons. The good agreement between simulated and experimental results highlights the potential of this approach for guiding and interpreting experiments, while also pointing out areas for further improvement.

cond-mat.mtrl-sci↗

arXiv2025

Mumott -- a Python package for the analysis of multi-modal tensor tomography data

Small and wide angle x-ray scattering tensor tomography are powerful methods for studying anisotropic nanostructures in a volume-resolved manner, and are becoming increasingly available to users of synchrotron facilities. The analysis of such experiments requires, however, advanced procedures and algorithms, which creates a barrier for the wider adoption of these techniques. Here, in response to this challenge, we introduce the mumott package. It is written in Python with computationally demanding tasks handled via just-in-time compilation using both CPU and GPU resources. The package is being developed with a focus on usability and extensibility, while achieving a high computational efficiency. Following a short introduction to the common workflow, we review key features, outline the underlying object-oriented framework, and demonstrate the computational performance. By developing the mumott package and making it generally available, we hope to lower the threshold for the adoption of tensor tomography and to make these techniques accessible to a larger research community.

cond-mat.mtrl-sci↗

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↗