arXiv subjects

Erik Fransson

Publications and source records attributed to Erik Fransson.

33 records · Page 2Linked to original sources

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.

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arXiv2023

Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr$_3$ and MAPbI$_3$

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.

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arXiv2023

Tuning the lattice thermal conductivity in van-der-Waals structures through rotational (dis)ordering

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 moiré 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.

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arXiv2023

Limits of the phonon quasi-particle picture at the cubic-to-tetragonal phase transition in halide perovskites

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.

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arXiv2023

Anharmonicity of the antiferrodistortive soft mode in barium zirconate BaZrO$_3$

Barium zirconate (BaZrO$_3$) is one of the very few perovskites that is claimed to retain an average cubic structure down to \SI{0}{\K}, while being energetically very close to an antiferrodistortive phase obtained by condensation of a soft phonon mode at the R point of the Brillouin zone boundary. In this work, we report a combined experimental and theoretical study of the temperature dependence of this soft phonon mode. Inelastic neutron and x-ray scattering measurements on single crystals show that it softens substantially from \SI{9.4}{\meV} at room temperature to \SI{5.6}{\meV} at \SI{2}{\K}. In contrast, the acoustic mode at the same R point is nearly temperature independent. The effect of the anharmonicity on the lattice dynamics is investigated non-perturbatively using direct dynamic simulations as well as a first-principles based self-consistent phonon theory, including quantum fluctuations of the atomic motion. By adding cubic and quartic anharmonic force constants, quantitative agreement with the neutron data for the temperature dependence of the antiferrodistortive mode is obtained. The quantum fluctuations of the atomic motion are found to be important to obtain the proper temperature dependence at low temperatures. The mean squared displacements of the different atoms are determined as function of temperature and are shown to be consistent with available experimental data. Adding anharmonicity to the computed fluctuations of the Ba-O distances also improves the comparison with available EXAFS data at \SI{300}{\K}.

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arXiv2023

Quantifying Dynamic Tilting in Halide Perovskites: Chemical Trends and Local Correlations

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.

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arXiv2023

Phase transitions in inorganic halide perovskites from machine learning potentials

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.

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arXiv2021

A tale of two phase diagrams: Interplay of ordering and hydrogen uptake in Pd-Au-H

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.

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arXiv2020

DYNASOR -- A tool for extracting dynamical structure factors and current correlation functions from molecular dynamics simulations

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.

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arXiv2020

Efficient construction of linear models in materials modeling and applications to force constant expansions

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.

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arXiv2019

Defects from phonons: Atomic transport by concerted motion in simple crystalline metals

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.

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arXiv2019

Thermal conductivity in intermetallic clathrates: A first principles perspective

Inorganic clathrates such as Ba$_8$Ga$_{x}$Ge$_{46-x}$ and Ba$_8$Al$_{x}$Si$_{46-x}$ commonly exhibit very low thermal conductivities. A quantitative computational description of this important property has proven difficult, in part due to the large unit cell, the role of disorder, and the fact that both electronic carriers and phonons contribute to transport. Here, we conduct a systematic analysis of the temperature and composition dependence of low-frequency modes associated with guest species in Ba$_8$Ga$_{x}$Ge$_{46-x}$ and Ba$_8$Al$_{x}$Si$_{46-x}$ ("rattler modes"), as well as of thermal transport in stoichiometric Ba$_8$Ga$_{16}$Ge$_{30}$. To this end, we account for phonon-phonon interactions by means of temperature dependent effective interatomic force constants (TDIFCs), which we find to be crucial in order to achieve an accurate description of the lattice part of the thermal conductivity. While the analysis of the thermal conductivity is often largely focused on the rattler modes, here, it is shown that at room temperatures modes with $\hbarω\gtrsim\,10\,\text{meV}$ account for 50\%\ of lattice heat transport. Finally, the electronic contribution to the thermal conductivity is computed, which shows the Wiedemann-Franz law to be only approximately fulfilled. As a result, it is crucial to employ the correct prefactor when separating electronic and lattice contributions for experimental data.

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arXiv2019

The hiphive package for the extraction of high-order force constants by machine learning

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.

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arXiv2019

icet - A Python library for constructing and sampling alloy cluster expansions

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.

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arXiv2017

atomicrex - A general purpose tool for the construction of atomic interaction models

We introduce atomicrex, an open-source code for constructing interatomic potentials as well as more general types of atomic-scale models. Such effective models are required to simulate extended materials structures comprising many thousands of atoms or more, because electronic structure methods become computationally too expensive at this scale. atomicrex covers a wide range of interatomic potential types and fulfills many needs in atomistic model development. As inputs, it supports experimental property values as well as \textit{ab initio} energies and forces, to which models can be fitted using various optimization algorithms. The open architecture of atomicrex allows it to be used in custom model development scenarios beyond classical interatomic potentials while thanks to its Python interface it can be readily integrated e.g., with electronic structure calculations or machine learning algorithms.

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