arXiv subjects

Erik Fransson

Publications and source records attributed to Erik Fransson.

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

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

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

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

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↗

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

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↗

arXiv2024

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↗

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

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↗

arXiv2023

Understanding correlations in BaZrO3:Structure and dynamics on the nano-scale

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.

cond-mat.mtrl-sci↗