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Designing coatings and films that can protect surfaces is important in a wide variety of applications from corrosion prevention to anti-fouling. These systems are challenging from a modeling perspective because they are invariably multicomponent, which quickly leads to an expansive design space. At a minimum, the system has a substrate, a film (often composed of a polymeric material), a ubiquitous carrier solvent, which may be either a vapor or liquid phase, and one or more contaminants. Each component has an impact on the effectiveness of coating. This paper focuses on films that are used as a barrier to surface contamination, but the results also extend to surface coatings that are designed to extract a low density species from the fluid phase as in liquid chromatography. A coarse-grained model is developed using Yukawa potentials that encompasses both repulsive and attractive interactions among the species. Classical density functional theory calculations are presented to show how contaminant adsorption is controlled by the molecular forces in the system. Two specific vectors through the parameter space are considered to address likely experimental manipulations that change either the solvent or the polymer in a system. We find that all the adsorption results can be unified by considering an appropriate combination of molecular parameters. As a result, these calculations provide a link between molecular interactions and film performance and may serve to guide the rational design of films. [ABSTRACT FROM AUTHOR]

Exploring potential two-dimensional monolayers with large photogalvanic effect (PGE) has been of great importance for developing self-powered optoelectronic devices. In this paper, we systematically investigate the generation of PGE photocurrent in chalcogen-based Janus XYZ monolayers (X/Y/Z = S, Se, Te; X ≠ Y ≠ Z) based on non-equilibrium Green's function formalism with density functional theory. The optimized Janus SSeTe, SeSTe, and TeSeS monolayers in the rectangular phase are shown stable and, respectively, possess 1.54, 1.49, and 1.74 eV indirect bandgaps. Illuminated by linearly polarized light, the PGE photocurrent without bias voltage can be collected in both armchair and zigzag directions. Unlike common Janus 2D materials with C3v symmetry, the photocurrent peak values of Janus XYZ monolayers do not come up with certain polarization angles, while the relations can be fitted by Iph = α sin(2θ) + β cos(2θ) + γ at each photon energy. Meanwhile, the maximum photoresponses of Janus SSeTe, SeSTe, and TeSeS monolayers are 2.02, 3.33, and 4.42 a20/photon, respectively. The relatively large PGE photocurrents and complicated polarization relations result from the lower symmetry of Janus XYZ monolayers. Moreover, the specific polarization angles for maximum photoresponses at each photon energy and the ratio between two transport directions are demonstrated, reflecting the anisotropy. Our results theoretically predict a potential Janus monolayer family for self-powered optoelectronic applications. [ABSTRACT FROM AUTHOR]

With density functional theory (DFT), it is possible to calculate the formation energy of charged point defects and in turn to predict a range of experimentally relevant quantities, such as defect concentrations, charge transition levels, or recombination rates. While prior efforts have led to marked improvements in the accuracy of such calculations, comparatively modest effort has been directed at quantifying their uncertainties. However, in the broader DFT research space, the development of Bayesian Error Estimation Functionals (BEEF) has enabled uncertainty quantification (UQ) for other properties. In this paper, we investigate the utility of BEEF as a tool for UQ of defect formation energies. We build a pipeline for propagating BEEF energies through a formation-energy calculation and test it on intrinsic defects in several materials systems spanning a variety of chemistries, bandgaps, and crystal structures, comparing to prior published results where available. We also assess the impact of aligning to a deep-level transition rather than to the VBM (valence band maximum). We observe negligible dependence of the estimated uncertainty upon a supercell size, though the relationship may be obfuscated by the fact that finite-size corrections cannot be computed separately for each member of the BEEF ensemble. Additionally, we find an increase in estimated uncertainty with respect to the absolute charge of a defect and the relaxation around the defect site without deep-level alignment, but this trend is absent when the alignment is applied. While further investigation is warranted, our results suggest that BEEF could be a useful method for UQ in defect calculations. [ABSTRACT FROM AUTHOR]

Direct observation of the capture cross section is challenging due to the need for extremely short filling pulses in the two-gate Deep-Level Transient Spectroscopy (DLTS). Simple estimation of the cross section can be done from DLTS and admittance spectroscopy data but it is not feasible to distinguish temperature dependence of pre-exponential and exponential parts of the emission rate equation with sufficient precision conducting a single experiment. This paper presents experimental data of deep levels in β-Ga2O3 that has been gathered by our group since 2017. Based on the gathered data, we propose a derivation of apparent activation energy (E a m) and capture cross section (σ n m) assuming the temperature dependent capture via the multiphonon emission model, which resulted in a strong correlation between E a m and σ n m according to the Meyer–Neldel rule, which allowed us to estimate low- and high-temperature capture coefficients C 0 and C 1 as well as capture barrier E b. It also has been shown that without considering the temperature dependence of capture cross section, the experimental values of σ n are overestimated by 1–3 orders of magnitude. A careful consideration of the data also allows to be more certain identifying deep levels by their "fingerprints" (E a and σ n) considering two additional parameters (E M N and σ 00) and to verify the density functional theory computation of deep-level recombination properties. [ABSTRACT FROM AUTHOR]

Direct observation of the capture cross section is challenging due to the need for extremely short filling pulses in the two-gate Deep-Level Transient Spectroscopy (DLTS). Simple estimation of the cross section can be done from DLTS and admittance spectroscopy data but it is not feasible to distinguish temperature dependence of pre-exponential and exponential parts of the emission rate equation with sufficient precision conducting a single experiment. This paper presents experimental data of deep levels in β-Ga2O3 that has been gathered by our group since 2017. Based on the gathered data, we propose a derivation of apparent activation energy (E a m) and capture cross section (σ n m) assuming the temperature dependent capture via the multiphonon emission model, which resulted in a strong correlation between E a m and σ n m according to the Meyer–Neldel rule, which allowed us to estimate low- and high-temperature capture coefficients C 0 and C 1 as well as capture barrier E b. It also has been shown that without considering the temperature dependence of capture cross section, the experimental values of σ n are overestimated by 1–3 orders of magnitude. A careful consideration of the data also allows to be more certain identifying deep levels by their "fingerprints" (E a and σ n) considering two additional parameters (E M N and σ 00) and to verify the density functional theory computation of deep-level recombination properties. [ABSTRACT FROM AUTHOR]

This paper presents the first implementation of a coupling between advanced wavefunction theories and molecular density functional theory (MDFT). This method enables the modeling of solvent effect into quantum mechanical (QM) calculations by incorporating an electrostatic potential generated by solvent charges into the electronic Hamiltonian. Solvent charges are deduced from the spatially and angularly dependent solvent particle density. Such a density is obtained through the minimization of the functional associated with the molecular mechanics (MM) Hamiltonian describing the interaction between the fluid particles. The introduced QM/MDFT framework belongs to QM/MM family of methods, but its originality lies in the use of MDFT as the MM solver, offering two main advantages. First, its functional formulation makes it competitive with respect to sampling-based molecular mechanics. Second, it preserves a molecular-level description lost in macroscopic continuum approaches. The excited state properties of water and formaldehyde molecules solvated into water have been computed at the selected configuration interaction (SCI) level. The excitation energies and dipole moments have been compared with experimental data and previous theoretical work. A key finding is that using the Hartree–Fock method to describe the solute allows for predicting the solvent charge around the ground state with sufficient precision for the subsequent SCI calculations of excited states. This significantly reduces the computational cost of the described procedure, paving the way for the study of more complex molecules. [ABSTRACT FROM AUTHOR]

The superadiabatic dynamical density functional theory (superadiabatic-DDFT) is a promising new method for the study of colloidal systems out-of-equilibrium. Within this approach, the viscous forces arising from interparticle interactions are accounted for in a natural way by explicitly treating the dynamics of the two-body correlations. For bulk systems subject to spatially homogeneous shear, we use the superadiabatic-DDFT framework to calculate the steady-state pair distribution function and the corresponding viscosity for low values of the shear-rate. We then consider a variant of the central approximation underlying this superadiabatic theory and obtain an inhomogeneous generalization of a rheological bulk theory due to Russel and Gast. This paper thus establishes for the first time a connection between DDFT approaches, formulated to treat inhomogeneous systems, and existing work addressing nonequilibrium microstructure and rheology in bulk colloidal suspensions. [ABSTRACT FROM AUTHOR]

In this paper, benchmark results are presented on the calculation of vertical electronic excitation energies using a long-range second-order polarization propagator approximation (SOPPA) description with a short-range density functional theory description based on the Perdew–Burke–Ernzerhof (PBE) functional. The excitation energies are investigated for 132 singlet states and 71 triplet states across 28 medium-sized organic molecules. The results show that overall SOPPA-srPBE always performs better than PBE and that SOPPA-srPBE performs better than SOPPA for singlet states, but slightly worse than SOPPA for triplet states when CC3 results are the reference values. [ABSTRACT FROM AUTHOR]

Since the 1940s, it has been known that diffusion in crystalline solids occurs due to lattice defects. The diffusion of defects can have a great impact on the processing and heat treatment of materials as the microstructural changes caused by diffusion can influence the material qualities and properties. It is, therefore, vital to be able to control the diffusion. This implies that we need a deep understanding of the interactions between impurities, matrix atoms, and intrinsic defects. The role of density functional theory (DFT) calculations in solid-state diffusion studies has become considerable. The main parameters to obtain in defect diffusion studies with DFT are formation energies, binding energies, and migration barriers. In particular, the utilization of the nudged elastic band and the dimer methods has improved the accuracy of these parameters. In systematic diffusion studies, the combination of experimentally obtained results and theoretical predictions can reveal information about the atomic diffusion processes. The combination of the theoretical predictions and the experimental results gives a unique opportunity to compare parameters found from the different methods and gain knowledge about atomic migration. In this Perspective paper, we present case studies on defect diffusion in wide bandgap semiconductors. The case studies cover examples from the three diffusion models: free diffusion, trap-limited diffusion, and reaction diffusion. We focus on the role of DFT in these studies combined with results obtained with the experimental techniques secondary ion mass spectrometry and deep-level transient spectroscopy combined with diffusion simulations. [ABSTRACT FROM AUTHOR]

To address the challenge of performance portability and facilitate the implementation of electronic structure solvers, we developed the basic matrix library (BML) and Parallel, Rapid O(N), and Graph-based Recursive Electronic Structure Solver (PROGRESS) library. The BML implements linear algebra operations necessary for electronic structure kernels using a unified user interface for various matrix formats (dense and sparse) and architectures (CPUs and GPUs). Focusing on density functional theory and tight-binding models, PROGRESS implements several solvers for computing the single-particle density matrix and relies on BML. In this paper, we describe the general strategies used for these implementations on various computer architectures, using OpenMP target functionalities on GPUs, in conjunction with third-party libraries to handle performance critical numerical kernels. We demonstrate the portability of this approach and its performance in benchmark problems. [ABSTRACT FROM AUTHOR]

Hydration and, in particular, the coordination number of a metal ion is of paramount importance as it defines many of its (bio)physicochemical properties. It is not only essential for understanding its behavior in aqueous solutions but also determines the metal ion reference state and its binding energy to (bio)molecules. In this paper, for divalent metal cations Ca 2 + , Cd 2 + , Cu 2 + , Fe 2 + , Hg 2 + , Mg 2 + , Ni 2 + , Pb 2 + , and Zn 2 + , we compare two approaches for predicting hydration numbers: (1) a mixed explicit/continuum DFT-D3//COSMO-RS solvation model and (2) density functional theory based ab initio molecular dynamics. The former approach is employed to calculate the Gibbs free energy change for the sequential hydration reactions, starting from [M( H 2 O)2]2+ aqua complexes to [M( H 2 O) 9]2+, allowing explicit water molecules to bind in the first or second coordination sphere and determining the most stable [M( H 2 O) n]2+ structure. In the latter approach, the hydration number is obtained by integrating the ion–water radial distribution function. With a couple of exceptions, the metal ion hydration numbers predicted by the two approaches are in mutual agreement, as well as in agreement with the experimental data. [ABSTRACT FROM AUTHOR]

This paper studies Density Functional Theory (DFT) models for homogeneous 1D materials in the 3D space. It follows the previous work [Gontier et al., Commun. Math. Phys. 388, 1475–1505 (2021)] about DFT models for homogeneous 2D materials in 3D. We show how to reduce the problem from a 3D energy functional to a 2D energy functional. The kinetic energy is treated as in the 2D material case by diagonalizing admissible states, and writing the kinetic energy as the infimum of a modified kinetic energy functional on reduced states. Besides, we treat here the Hartree interaction term in 2D, and show how to properly define the mean-field potential, through Riesz potential. We then show the well-posedness of the reduced model and present some numerical illustrations. [ABSTRACT FROM AUTHOR]

The cyclic two-step process, comprised of energetic H2 plasma followed by HF wet clean or in situ NF3 plasma, demonstrates Si3N4 layer-by-layer removal capability exceeding 10 nm per cycle, surpassing typical atomic layer etch methods by an order of magnitude. In this paper, we investigated the surface reaction mechanisms via first principle density functional theory simulations and surface analysis. The results unveiled that energetic H2 plasma, in the first step, selectively removes nitrogen (N) in preference to silicon (Si), generating ammonia (NHx) and transforming Si3N4 into SiON upon exposure to air, which becomes removable by HF wet clean in the second step. For the second step employing in situ NF3 plasma, it further leverages H-passivated surfaces to enhance NF3 dissociation and provide alternative reaction pathways to yield volatile byproducts such as SiHF3 and SiFx, thereby significantly improving nitride removal efficiency. [ABSTRACT FROM AUTHOR]

The dynamics of an inertial confinement fusion capsule, or of a stellar or planet interior, obey a very similar set of equations: magneto-radiative-hydrodynamic equations. The solutions of these equations, however, depend entirely on the transport properties associated with the different materials at play. To properly model the dynamics of these systems, it is necessary to determine with high accuracy the transport coefficients of several materials over a large range of thermodynamic conditions. Experimental capabilities in this respect are still limited due to the nature of the microphysics at play and the extreme conditions involved. Numerical simulations are thus necessary, and in this respect, molecular dynamics simulations based on density functional theory offer exquisite possibilities to constrain the transport properties in the warm to hot dense matter regime. In this paper, we report the methodology used to extract different transport properties based on molecular dynamics performed with the software Abinit. The examples shown are based on the specific cases identified for the purpose of the second charged-particle transport code comparison workshop. [ABSTRACT FROM AUTHOR]

The method of density functional theory (DFT) combined with Hubbard U correction has been employed in wide-ranging applications in correlated systems. Although the DFT+U method can dramatically improve the treatment of many strongly correlated systems, careful attention must be paid to those parameters that significantly influence results. By varying the local-projection size with the muffin-tin radius, we examine the effect of the Hubbard U correction on lattice parameters, electronic structure, and the relative stability of TiO2 between rutile and anatase. Our results show that different ranges of the local projection can produce strikingly different quantitative results, leading to distinct conclusions. The quantitative discrepancies are attributed to the sensitivity of the occupancy numbers for the correlated orbitals toward the size of the projection subspace. [ABSTRACT FROM AUTHOR]

In the present work, 15 Mo/VC interfaces were investigated using first-principles calculations based on density functional theory. Four possible interface orientations, two terminations, and three stacking sites were considered. The adhesion energy (Wad) and interfacial energy (Eint) of these interface models were computed. The results indicate that the C-terminated hollow-site Mo(110)/VC(111) interface exhibits the highest stability with a larger Wad value of 10.64 J m−2 and the lowest Eint value of 2.98 J m−2, followed by the V-terminated central-site Mo(211)/VC(220) interface. Analysis of the electronic structure reveals the formation of strong polar covalent bonds at these interfaces. Additionally, simulations of tensile fracture processes were performed, demonstrating that at strains reaching 22% and 32%, respectively, the ideal tensile strengths for the C-terminated hollow-site Mo(110)/VC(111) interface and V-terminated central-site Mo(211)/VC(220) interface are ∼26.01 and 35.53 GPa. In particular, in the C-terminated hollow-site Mo(110)/VC(111) interface, fracture occurs in the Mo slabs due to concentrated strain when external strain is applied; meanwhile, uniform strain is observed in both Mo(211) and VC(200) slabs within this system. Notably, the V-terminated central-site Mo(211)/VC (220) interface demonstrates excellent tensile strength as well as toughness. These findings suggest that explaining solely based on adhesion work is insufficient to account for the observed tensile strength at these interfaces. [ABSTRACT FROM AUTHOR]

With the development of science and technology and the improvement of hardware computing power, the application of large models in the field of artificial intelligence (AI) has become a current research hotspot Among the focal points in the field of deep learning, AI for science is one of the highlighted areas, utilizing deep learning methods for pattern recognition, anomaly detection, predictive analysis, and more on a large scale of scientific data. In the realm of materials science, the structure of crystals is composed of edges and nodes, making it readily representable as a graph. In previous research, some typical models, such as the MEGNet model, utilized their graph neural network features to fit computational results based on density functional theory for predicting various material properties. Building on this concept, the authors propose a novel graph neural network (GNN) model, optimized with a Multi-Head Self-Attention (MHSA) mechanism, for predicting materials data with crystal structures. This model is named self-attention enhanced graph neural network. The model segments the input data into three parts: edges, nodes, and global features. The graph convolutional layer module is primarily used for aggregating node, edge, and global features, learning node representations, and capturing higher-order neighborhood information through multiple layers of GNN. The MHSA component allows nodes to learn global dependencies, providing different representation subspaces for the nodes. In comparison with other machine learning and deep learning models, the results indicate an improvement in the predictive accuracy of this model. A new graph neural network (GNN) model called Self-Attention Enhanced Graph Neural Network (SA-GNN) is proposed for predicting the properties of materials with crystal structures. This model incorporates multi-head self-attention to allow nodes to learn global dependencies and generate different representational subspaces. Compared to other machine learning and deep learning models, the results show improved predictive accuracy, demonstrating the potential of graph networks combined with self-attention for modeling crystal material data. [ABSTRACT FROM AUTHOR]

The present article has utilized the WIEN2k computational code to examine the structural, elastic, electronic, magnetic, thermoelectric, and dynamic aspects of the CoCrS Half-Heusler (HH) compound. In this work, calculations have been performed by utilizing the full-potential linearized augmented plane wave method using density functional theory. The electronic bandgap was better interpreted by using the modified Becke–Johnson exchange–correlation functional. We evaluated various electronic properties of the CoCrS HH compound, including band structure plots and density of states. Furthermore, we examined the magnetic characteristics through the computation of magnetic moments and the examination of the spin-polarized electronic state behavior. We also determined the elastic properties of the CoCrS HH compound. These properties, which include stiffness, resilience, and general stability, provide important information about how the material responds to mechanical deformation. Moreover, we explored the electronic structure and found that type 5 of CoCrS exhibits a metallic behavior. In addition, we examined the compound's thermoelectric properties. Finally, the dynamical properties indicate that type 5 of CoCrS is dynamically stable. [ABSTRACT FROM AUTHOR]

Two-dimensional (2D) MXenes, having comparable transport properties like graphene and a wide spectrum application, are often limited to being used in optoelectronics due to metallic bandgap. Here, by employing density functional theory we report the bandgap engineering and tuning optoelectronic properties through modulating the anions of novel 2D spinel Hf3X2O2 (X = N, P and As) MXenes structures and show that the material class can be among the few semiconducting MXenes. Phonon spectra and cohesive energies confirm that these structures are dynamically stable and chemically exothermic. Modulating anions X = N, P, and As in Hf3X2O2, the electronic bandgaps are found ∼0.46 eV for N, metallic for P, and ∼48 meV for As atoms, suggesting the semiconducting, metallic, and semi-metallic MXenes. The biaxial strains are incorporated to tune the features: In the Hf3N2O2 structure, the bandgap is increased with both compressive and tensile strains, while for the Hf3As2O2 structure, the gap decreased at the GGA-PBE level. For Hf3P2O2 structures, the bandgaps are all metallic irrespective of pristine or biaxial strain. Spin–orbit coupling SOC+GGA reveals that Hf3N2O2 is highly spin responsive while Hf3As2O2 shows semi-metal-to-metallic bandgap transition for pristine as well as biaxial strained conditions. From optical properties analysis, optical absorptions are found located in the visible spectral regions that are also highly receptive to biaxial strains. These properties we have unleashed for the novel Hf3X2O2 (X = N, P, As) semiconducting MXene, thus, show the potentiality of the utilization of the material class in nanoelectronics and optoelectronics applications. [ABSTRACT FROM AUTHOR]

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors. [ABSTRACT FROM AUTHOR]

Molybdenum clusters, characterized by their unique structure and intriguing catalytic properties, have gained significant attention in recent years. In several existing studies, density functional theory (DFT) methods have been used to find the lowest energy Mo clusters and explore their electronic and magnetic structure. In all cases, with the exception of a single recent study, where a genetic algorithm was employed, initial geometries of the clusters, prior to geometry optimization, were chosen using heuristic approaches based on symmetry considerations and known structures. DFT calculations were performed using different types of pseudopotentials, from soft to hard, and different types of basis sets. However, no comprehensive study has yet been carried out in which a DFT method with the best control on its precision would be complemented by a reliable global minimum search method to find the lowest energy Mo clusters. In this work, we employ a combination of a plane wave-based DFT method and ab initio random structure searching technique to find the lowest energy clusters of up to 10 Mo atoms. In each case, the search has been performed for clusters with different spin multiplicities, which enabled us to explore their magnetic structure. The results are compared for both hard and soft pseudopotentials stressing the importance of treating more electrons explicitly, in agreement with some of the previous studies. For most of the low-energy magnetic structures found, we investigate the distribution of their spin densities, and for all low energy clusters, we confirm their stability by calculating their vibrational structure. For a few smallest clusters, the results of multiconfigurational quantum chemistry calculations are also discussed. Finally, free energies of the Mo clusters, within the quasi-harmonic approximation, are also calculated and discussed. [ABSTRACT FROM AUTHOR]

In this work, the optoelectronic properties of the getchellite crystal, a layered semiconducting disordered alloy with the chemical formula AsSbS3, are probed by a combination of complementary spectroscopic techniques, i.e., x-ray photoelectron spectroscopy together with a photoreflectance and a transmission spectroscopy. The experimental results are supported by a calculation based on density functional theory (DFT). The sample is an intrinsically p-type semiconductor whose optical properties are dominated by a direct transition. The energy of this transition is highly decreasing, by 0.22 eV, when the sample temperature increases from 20 to 300 K. A calculated band structure together with the transmission results reveals that of about 50 meV below the direct optical transition, an indirect one occurs associated with the fundamental bandgap of AsSbS3. Finally, the change in the fundamental bandgap character from indirect to direct during the reduction in material thickness from bulk to monolayer is demonstrated by the DFT calculations. [ABSTRACT FROM AUTHOR]

In the present work, we choose the carbon-based halides CX4 (X = Br, Cl) to conduct a study on the ground-state properties using first-principles calculations based on density functional theory. We provide a comparative study for bromide and chloride materials using standard generalized gradient approximation functionals. A total of 80 atoms have been simulated on a single unit cell. A direct bandgap energy of 3.28 (4.64) eV for X = Br (X = Cl) with Z–Z transition is found on the studied materials. According to our partial density of states plot, a hybridization of s orbitals on C and X atoms is observed. Metallic properties have also been identified. A low reflectivity of 6% (12%) has been measured, which presents excellent material absorption. Such a material is also determined to be brittle, based on the calculated Pugh's ratio. The reflectivity (ductility) tends to increase as the X element increases down the Period Table. The negative Poisson's ratio found also suggests that the materials could be used as an electrode for metal-ion (e.g., Li, K, and Na) batteries. [ABSTRACT FROM AUTHOR]

The magnetostructural phase transition in Fe50Al50 alloy with chemically ordered paramagnetic B2 and disordered ferromagnetic (FM) A2 phase has applications in spintronics such as phase-change magnetic memory and magnonic devices. We first conducted a systematic first-principles density functional theory study of the A2 and B2 phases in the Fe50Al50 alloy. A theoretical understanding of this equiatomic alloy's electronic and spin-dynamical properties leads us to the experimental exploration of the FM A2 phase. Therefore, we deposit the 50 nm Fe50Al50 alloy thin film using sputtering and investigate the influence of the Ta buffer layer on its structural and magnetic properties. Our results reveal that the film with a buffer layer exhibits the A2 phase with appreciably higher saturation magnetization (848 emu/cc) than the film without a buffer layer (576 emu/cc). However, the surface roughness and Gilbert damping (α) slightly increase with the presence of the buffer layer from 0.41 to 0.56 nm and 4.35 × 10−3–4.94 × 10−3, respectively. The enhancement in α is due to extrinsic contributions induced by surface inhomogeneities. [ABSTRACT FROM AUTHOR]

We analyze microscopic nonlinear optical response of periodic structures within the Floquet–Bloch formalism. The analysis is focused on the real-space distributions of optically induced charge and electron current density within the unit cell of a crystal. We demonstrate that the time-reversal symmetry of a crystal determines the phases of the temporal oscillations of these distributions. We further analyze their spatial symmetries and connection to macroscopic optical response. We illustrate our study with ab initio calculations that combine density functional theory with the Floquet–Bloch formalism. The calculations provide time-dependent optically induced charge distributions and electron current densities within the unit cells of a crystal with inversion symmetry MgO and a crystal without inversion symmetry GaAs in response to a strong-field excitation. The real-space, microscopic view on nonlinear optical response provides insightful information about the strong field–matter interaction. [ABSTRACT FROM AUTHOR]