Anharmonic stabilization and lattice heat transport in rocksalt β\beta-GeTe

Peierls-Boltzmann transport equation, coupled with third-order anharmonic lattice dynamics calculations, has been widely used to model lattice thermal conductivity ($\kappa_{l}$) in bulk crystals. However, its application to materials with structural phase transition at relatively high temperature is fundamentally challenged by the presence of lattice instabilities (imaginary phonon modes). Additionally, its accuracy suffers from the absence of higher-than-third-order phonon scattering processes, which are important near/above the Debye temperature. In this letter, we present an effective scheme that combines temperature-induced anharmonic phonon renormalization and four-phonon scattering to resolve these two theoretical challenges. We apply this scheme to investigate the lattice dynamics and thermal transport properties of GeTe, which undergoes a second-order ferroelectric phase transition from rhombohedral $\alpha$-GeTe to rocksalt $\beta$-GeTe at about 700~K. Our results on the high-temperature phase $\beta$-GeTe at 800~K confirm the stabilization of $\beta$-GeTe by temperature effects. We find that considering only three-phonon scattering leads to significantly overestimated $\kappa_{l}$ of 3.8~W/mK at 800~K, whereas including four-phonon scattering reduces $\kappa_{l}$ to 1.7~W/mK, a value comparable with experiments. To explore the possibility to further suppress $\kappa_{l}$, we show that alloying $\beta$-GeTe with heavy cations such as Pb and Bi can effectively reduce $\kappa_{l}$ to about 1.0~W/mK, whereas sample size needs to be around 10nm through nanostructuring to achieve a comparable reduction of $\kappa_{l}$

Renormalized Lattice Dynamics and Thermal Transport in VO2_{2}

Vanadium dioxide (VO$_{2}$) undergoes a first-order metal-insulator transition (MIT) upon cooling near room temperature, concomitant with structural change from rutile to monoclinic. Accurate characterization of lattice vibrations is vital for elucidating the transition mechanism. To investigate the lattice dynamics and thermal transport properties of VO$_{2}$ across the MIT, we present a phonon renormalization scheme based on self-consistent phonon theory through iteratively refining vibrational free energy. Using this technique, we compute temperature-dependent phonon dispersion and lifetimes, and point out the importance of both magnetic and vibrational entropy in driving the MIT. The predicted phonon dispersion and lifetimes show quantitative agreement with experimental measurements. We demonstrate that lattice thermal conductivity of rutile VO$_{2}$ is nearly temperature independent as a result of strong intrinsic anharmonicity, while that of monoclinic VO$_{2}$ varies according to $1/T$. Due to phonon softening and enhanced scattering rates, the lattice thermal conductivity is deduced to be substantially lower in the rutile phase, suggesting that Wiedemann-Franz law might not be strongly violated in rutile VO$_{2}$

Theory of Thermal Relaxation of Electrons in Semiconductors

We compute the transient dynamics of phonons in contact with high energy "hot" charge carriers in 12 polar and non-polar semiconductors, using a first-principles Boltzmann transport framework. For most materials, we find that the decay in electronic temperature departs significantly from a single-exponential model at times ranging from 1 ps to 15 ps after electronic excitation, a phenomenon concomitant with the appearance of non-thermal vibrational modes. We demonstrate that these effects result from the slow thermalization within the phonon subsystem, caused by the large heterogeneity in the timescales of electron-phonon and phonon-phonon interactions in these materials. We propose a generalized 2-temperature model accounting for the phonon thermalization as a limiting step of electron-phonon thermalization, which captures the full thermal relaxation of hot electrons and holes in semiconductors. A direct consequence of our findings is that, for semiconductors, information about the spectral distribution of electron-phonon and phonon-phonon coupling can be extracted from the multi-exponential behavior of the electronic temperature

A Deep Learning Model for Atomic Structures Prediction Using X-ray Absorption Spectroscopic Data

A deep neural network (DNN) model consisting of two hidden layers was proposed for predicting the immediate environments of specific atoms based on X-ray absorption near-edge spectra (XANES). The output layer of the DNN can be adjusted to form a classifier or regressor, to predict the local and overall coordination environments, respectively. Using Li3FeO3.5 as a model system, it was demonstrated that the prediction accuracy of the DNN classifier is higher than 98%, and the predictions of the DNN regressor also showed notable agreement with the ground truth. Therefore, despite its simplicity, this DNN architecture can be expected to be generally capable of predicting the structural properties of various systems. Fine tuning of the hyperparameters, bias-variance tradeoff, and strategies to enrich the versatility of the model were also discussed.Comment: 11 pages, 4 figure

Lattice thermal transport in group II-alloyed PbTe

PbTe, one of the most promising thermoelectric materials, has recently demonstrated thermoelectric figure of merit ($ZT$) of above 2.0 when alloyed with group II elements. The improvements are due mainly to significant reduction of lattice thermal conductivity ($\kappa_{l}$), which was in turn attributed to nanoparticle precipitates. However, a fundamental understanding of various phonon scattering mechanisms within the bulk alloy is still lacking. In this work, we apply the newly-developed density-functional-theory (DFT)-based compressive sensing lattice dynamics (CSLD) approach to model lattice heat transport in PbTe, MTe, and Pb$_{0.94}$M$_{0.06}$Te (M=Mg, Ca, Sr and Ba), compare our results with experimental measurements, with focus on strain effect and mass disorder scattering. We find that (1) CaTe, SrTe and BaTe in the rock-salt structure exhibit much higher $\kappa_{l}$ than PbTe, while MgTe in the same structure shows anomalously low $\kappa_{l}$; (2) lattice heat transport of PbTe is extremely sensitive to static strain induced by alloying atoms in solid solution form; (3) mass disorder scattering plays a major role in reducing $\kappa_{l}$ for Mg/Ca/Sr-alloyed PbTe through strongly suppressing the lifetimes of intermediate- and high-frequency phonons, while for Ba-alloyed PbTe, precipitated nanoparticles are also important

Methodology of Parameterization of Molecular Mechanics Force Field From Quantum Chemistry Calculations using Genetic Algorithm: A case study of methanol

In molecular dynamics (MD) simulation, force field determines the capability of an individual model in capturing physical and chemistry properties. The method for generating proper parameters of the force field form is the key component for computational research in chemistry, biochemistry, and condensed-phase physics. Our study showed that the feasibility to predict experimental condensed phase properties (i.e., density and heat of vaporization) of methanol through problem specific force field from only quantum chemistry information. To acquire the satisfying parameter sets of the force field, the genetic algorithm (GA) is the main optimization method. For electrostatic potential energy, we optimized both the electrostatic parameters of methanol using the GA method, which leads to low deviations of between the quantum mechanics (QM) calculations and the GA optimized parameters. We optimized the van der Waals (vdW) parameters both using GA and guided GA methods by calibrating interaction energy of various methanol homo-clusters, such as nonamers, undecamers, or tridecamers. Excellent agreement between the training dataset from QM calculations (i.e., MP2) and GA optimized parameters can be achieved. However, only the guided GA method, which eliminates the overestimation of interaction energy from MP2 calculations in the optimization process, provides proper vdW parameters for MD simulation to get the condensed phase properties (i.e., density and heat of vaporization) of methanol. Throughout the whole optimization process, the experimental value were not involved in the objective functions, but were only used for the purpose of justifying models (i.e., nonamers, undecamers, or tridecamers) and validating methods (i.e., GA or guided GA). Our method shows the possibility of developing descriptive polarizable force field using only QM calculations.Comment: not submitted to anywhere else by July 201

Computational prediction of lattice thermal conductivity -- a comparison of molecular dynamics and Boltzmann transport approaches

The predictive modeling of lattice thermal conductivity is of fundamental importance for the understanding and design of materials for a wide range of applications. Two major approaches, namely molecular dynamics (MD) simulations and calculations solving approximately the Boltzmann transport equation (BTE), have been developed to compute the lattice thermal conductivity. We present a detailed direct comparison of these two approaches, using as prototypical cases MgO and PbTe. The comparison, carried out using empirical potentials, takes into account the effects of fourth order phonon scattering, temperature-dependent phonon frequencies (phonon renormalization), and investigates the effects of quantum vs. classical statistics. We clarify that equipartition, as opposed to Maxwell Boltzmann, govern the statistics of phonons in MD simulations. We find that lattice thermal conductivity values from MD and BTE show an apparent, satisfactory agreement; however such an agreement is the result of error cancellations. We also show that the primary effect of statistics on thermal conductivity is via the scattering rate dependence on phonon populations

Atomistic manipulation of reversible oxidation and reduction in Ag by electron beam

Employing electrons for direct control of nanoscale reaction is highly desirable since it provides fabrication of nanostructures with different properties at atomic resolution and with flexibility of dimension and location. Here, applying in situ transmission electron microscopy, we show the reversible oxidation and reduction kinetics in Ag, well controlled by changing the dose rate of electron beam. Aberration-corrected high-resolution transmission electron microscopy observation reveals that O atoms are preferably inserted and extracted along the {111} close-packed planes of Ag, leading to the nucleation and decomposition of nanoscale Ag2O islands on the Ag substrate. By controlling electron beam size and dose rate, we demonstrated fabrication of an array of 3 nm Ag2O nanodots in an Ag matrix. Our results open up a new pathway to manipulate atomistic reaction with electron beam towards the precise fabrication of nanostructures for device applications

Defect Physics of Pseudo-cubic Mixed Halide Lead Perovskites from First Principles

Owing to the increasing popularity of lead-based hybrid perovskites for photovoltaic (PV) applications, it is crucial to understand their defect physics and its influence on their optoelectronic properties. In this work, we simulate various point defects in pseudo-cubic structures of mixed iodide-bromide and bromide-chloride methylammonium lead perovskites with the general formula MAPbI_{3-y}Br_{y} or MAPbBr_{3-y}Cl_{y} (where y is between 0 and 3), and use first principles based density functional theory computations to study their relative formation energies and charge transition levels. We identify vacancy defects and Pb on MA anti-site defect as the lowest energy native defects in each perovskite. We observe that while the low energy defects in all MAPbI_{3-y}Br_{y} systems only create shallow transition levels, the Br or Cl vacancy defects in the Cl-containing pervoskites have low energy and form deep levels which become deeper for higher Cl content. Further, we study extrinsic substitution by different elements at the Pb site in MAPbBr_{3}, MAPbCl_{3} and the 50-50 mixed halide perovskite, MAPbBr_{1.5}Cl_{1.5}, and identify some transition metals that create lower energy defects than the dominant intrinsic defects and also create mid-gap charge transition levels.Comment: 10 pages, 5 figures, 1 supplementary information fil

Plot2Spectra: an Automatic Spectra Extraction Tool

Different types of spectroscopies, such as X-ray absorption near edge structure (XANES) and Raman spectroscopy, play a very important role in analyzing the characteristics of different materials. In scientific literature, XANES/Raman data are usually plotted in line graphs which is a visually appropriate way to represent the information when the end-user is a human reader. However, such graphs are not conducive to direct programmatic analysis due to the lack of automatic tools. In this paper, we develop a plot digitizer, named Plot2Spectra, to extract data points from spectroscopy graph images in an automatic fashion, which makes it possible for large scale data acquisition and analysis. Specifically, the plot digitizer is a two-stage framework. In the first axis alignment stage, we adopt an anchor-free detector to detect the plot region and then refine the detected bounding boxes with an edge-based constraint to locate the position of two axes. We also apply scene text detector to extract and interpret all tick information below the x-axis. In the second plot data extraction stage, we first employ semantic segmentation to separate pixels belonging to plot lines from the background, and from there, incorporate optical flow constraints to the plot line pixels to assign them to the appropriate line (data instance) they encode. Extensive experiments are conducted to validate the effectiveness of the proposed plot digitizer, which shows that such a tool could help accelerate the discovery and machine learning of materials properties