Materials Science (cond-mat.mtrl-sci)

Abstract: Like single perovskites, halide double perovskites (HDP) have truly emerged as efficient optoelectronic materials since they display superior stability and are free of toxicity. However, challenges still exist due to either wide and indirect bandgaps or parity-forbidden transitions in many of them. The lack of understanding in chemical bonding and the formation of parity-driven valence and conduction band edge states have hindered the design of optoelectronically efficient HDPs. In this study, we have developed a theoretical workflow using a multi-integrated approach involving ab-initio density functional theory (DFT) calculations, model Hamiltonian studies, and molecular orbital picture leading to momentum matrix element (MME) estimation. This workflow gives us detailed insight into chemical bonding and parity-driven optical transition between edge states. In the process, we have developed a band-projected molecular orbital picture (B-MOP) connecting free atomic orbital states obtained at the Hartree-Fock level and orbital-resolved DFT bands. From the B-MOP, we show that the nearest neighbor cation-anion interaction determines the position of atom-resolved band states, while the second neighbor cation-cation interactions determine the shape and width of band dispersion and, thereby, MME. The latter is critical to quantify the optical absorption coefficient. Considering both B-MOP and MME, we demonstrate a mechanism of tailoring bandgap and optical absorptions through chemical doping at the cation sites. Furthermore, the cause of bandgap bowing, a common occurrence in doped HDPs, is explained by ascribing it to chemical effect and structural distortion.

Abstract: Machine learning techniques are attractive options for developing highly-accurate automated analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapidly generating complex nanoscale atomic structures, and develop an end-to-end workflow for creating large simulated databases for training neural networks. Construction Zone enables fast, systematic sampling of realistic nanomaterial structures, and can be used as a random structure generator for simulated databases, which is important for generating large, diverse synthetic datasets. Using HRTEM imaging as an example, we train a series of neural networks on various subsets of our simulated databases to segment nanoparticles and holistically study the data curation process to understand how various aspects of the curated simulated data -- including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions -- affect model performance across several experimental benchmarks. Using our results, we are able to achieve state-of-the-art segmentation performance on experimental HRTEM images of nanoparticles from several experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data.

Abstract: Single crystals of the novel strontium osmate Sr3Os4O14 have been grown by the hydrothermal method using opposed anvil high-pressure and high-temperature technique. The reaction took place in sealed gold capsules at 3 GPa and a temperature of 1100 C, with water acting as a solvent. The employed method yields up to 1 mm crystals with quite uncommon double-terminated morphologies. The crystal structure was identified as tetragonal by single-crystal X-ray diffraction, with lattice parameters a = 12.2909(8) A and c = 7.2478(5) A. The structural analysis suggests P42nm or P42/mnm as a possible space group. In general, the structure belongs to the pyrochlore type and is composed of a network of symmetrically arranged OsO6 octahedra. Resistivity measurements evidence a metallic behavior, accompanied by a temperature-independent paramagnetism. Heat capacity measurements reveal a slightly enhanced value of the Sommerfeld coefficient 34 mJ/mol K2. Superconductivity has not been observed down to 2 K.

Abstract: Localized atomic orbitals are the preferred basis-set choice for large-scale explicit correlated calculations, and high-quality hierarchical correlation-consistent basis sets are a prerequisite for correlated methods to deliver numerically reliable results. At present, Numeric Atom-centered Orbital (NAO) basis sets with valence correlation consistency (VCC), designated as NAO-VCC-$n$Z, are only available for light elements from hydrogen (H) to argon (Ar) (\textit{New J. Phys.} \textbf{15}, 123033, (2013) ). In this work, we extend this series by developing NAO-VCC-$n$Z basis sets for krypton (Kr), a prototypical element in the fourth row of periodic table. We demonstrate that NAO-VCC-$n$Z basis sets facilitate the convergence of electronic total-energy calculations using the Random Phase Approximation (RPA), which can be used together with a two-point extrapolation scheme to approach the complete-basis-set (CBS) limit. Notably, the Basis Set Superposition Error (BSSE) associated with the newly generated NAO basis sets is minimal, making them suitable for applications where BSSE correction is either cumbersome or impractical to do. After confirming the reliability of NAO basis sets for Kr, we proceed to calculate the Helmholtz free energy for Kr crystal at the theoretical level of RPA plus renormalized single excitation (rSE) correction. From this, we derive the pressure-volume ($P$-$V$) diagram, which shows excellent agreement with the latest experimental data. Our work demonstrates the capability of correlation-consistent NAO basis sets for heavy elements, paving the way toward numerically reliable correlated calculations for bulk materials.

Abstract: Crystalline CaF2 is drawing huge attentions due to its great potential of being the gate dielectric of two-dimensional (2D) material MOSFETs. It is deemed to be much superior than boron nitride and traditional SiO2 because of its larger dielectric constant, wider band gap, and lower defect density. Nevertheless, the CaF2-based MOSFETs fabricated in experiment still present notable reliability issues, and the underlying reason remains unclear. Here we studied the various intrinsic defects and adsorbates in CaF2/MoS2 and CaF2/MoSi2N4 interface systems to reveal the most active charge trapping centers in CaF2-based 2D material MOSFETs. An elaborate Table that comparing the importance of different defects in both n-type and p-type device is provided. Most impressively, the oxygen molecules adsorbed at the interface or surface, which are inevitable in experiments, are as active as the intrinsic defects in channel materials, and they can even change the MoSi2N4 to p-type spontaneously. These results mean that it is necessary to develop high vacuum packaging process as well as preparing high-quality 2D materials for better device performance.

Abstract: In the search for stable lead (Pb) free perovskites, Vacancy ordered double perovskite (VODP), A$_2$BX$_6$ has emerged as a promising class of materials for solar harvesting owing to their nontoxicity, better stability, and unique optoelectronic properties. Here, we present the stability and the key physical attributes of few selected compounds in a systematic manner using state-of-the-art first-principle calculations. A careful structural and stability analysis via simulating convex hull and compositional phase diagrams for different structural prototypes discloses 14 stable and 1 metastable compounds in this class. The electronic structure calculations using hybrid functional reveals six compounds to acquire band gap in the ideal visible region. These six compounds, namely Cs$_2$SnI$_6$, Cs$_2$PdI$_6$, Cs$_2$TeI$_6$, Cs$_2$TiI$_6$, Cs$_2$PtI$_6$, and Cs$_2$PdBr$_6$, show high optical absorption ($\approx$ 10$^{5}$ cm $^{-1}$) giving rise to high spectroscopic limited maximum efficiency, SLME (15-23\%) in the thin-film thickness range. Close inspection of transport properties reveals polar optical phonon scattering to be the dominant mechanism limiting the overall mobility. Further analysis of the polaron excitations discloses the possibility of large polaron formation at low to moderate defect concentrations. At high defect concentrations, ionized impurity scattering takes over. This suggests that, a simulation based guided control of defect concentrations during synthesis can yield a desired candidate for promissing device application. Additionally, few selected compounds show moderate to high electron mobility values ($\sim$13-63 cm$^2$V$^{-1}$ s$^{-1}$) at room temperature. Overall, the present study paves an important path to help design VODP as Pb-free potential candidates for future optoelectronic applications.

Abstract: Xenes, two-dimensional (2D) monolayers composed of a single element, with graphene as a typical representative, have attracted widespread attention. Most of the previous Xenes, X from group-IIIA to group-VIA elements have bonding characteristics of covalent bonds. In this work, we for the first time unveil the pivotal role of a halogen bond, which is a distinctive type of bonding with interaction strength between that of a covalent bond and a van der Waals interaction, in 2D group-VIIA monolayers. Combing the ingenious non-edge-to-edge tiling theory and state-of-art ab initio method with refined local density functional M06-L, we provide a precise and effective bottom-up construction of 2D iodine monolayer sheets, iodinenes, primarily governed by halogen bonds, and successfully design a category of stable iodinenes, encompassing herringbone, Pythagorean, gyrated truncated hexagonal, i.e. diatomic-kagome, and gyrated hexagonal tiling pattern. These iodinene structures exhibit a wealth of properties, such as flat bands, nontrivial topology, and fascinating optical characteristics, offering valuable insights and guidance for future experimental investigations. Our work not only unveils the unexplored halogen bonding mechanism in 2D materials but also opens a new avenue for designing other non-covalent bonding 2D materials.

Abstract: The discoveries of two-dimensional ferromagnetism and magnetic semiconductors highly enrich the magnetic material family for constructing spin-based electronic devices but with an acknowledged challenge that the Curie temperature (Tc) is usually far below room temperature. Many efforts such as voltage control and magnetic ion doping are currently underway to enhance the functional temperature, in which the involvement of additional electrodes or extra magnetic ions limits their plenty of applications in practical devices. Here we demonstrate that the magnetic proximity, a robust effect but with elusive mechanisms, can induce room-temperature ferromagnetism at the interface between sputtered Pt and semiconducting Fe3GeTe2, both of which do not show ferromagnetism at 300 K. The independent electrical and magnetization measurements, structure analysis, and control samples with Ta highlighting the role of Pt confirm that the ferromagnetism with the Tc of above 400 K arises from the Fe3GeTe2/Pt interfaces, rather than Fe aggregation or other artificial effects. Moreover, contrary to conventional ferromagnet/Pt structures, the spin current generated by the Pt layer is enhanced more than two times at the Fe3GeTe2/Pt interfaces, indicating the potential applications of the unique proximity effect in building high-efficient spintronic devices. These results may pave a new avenue to create room-temperature functional spin devices based on low-Tc materials and provide clear evidences of magnetic proximity effects by using non-ferromagnetic materials.

Abstract: SrMnO$_{3}$ (SMO) is a magnetic insulator and predicted to exhibit a multiferroic phase upon straining. Strained films of SMO display a wide range of magnetic orders, ranging from G-type to C-and A-type, indicative of competing magnetic interactions. The potential of spin Hall magnetoresistance (SMR) is exploited as an electrical probe for detecting surface magnetic order, to read surface magnetic moments in SMO and its spatial variation, by designing and positioning electrodes of different sizes on the film. The findings demonstrate antiferromagnetic domains with different magnetocrystalline anisotropies along with a ferromagnetic order, where the magnetization arises from double exchange mediated ferromagnetic order and canted antiferromagnetic moments. Further, from a complete analysis of the SMR, a predominance of antiferromagnetic domain sizes of 3.5 $\mu$m$^2$ is extracted. This work enhances the applicability of SMR in unraveling the richness of correlation effects in complex oxides, as manifested by the detection of coexisting and competing ground states and lays the foundation for the study of magnon transport for different magnetoelectric based computing applications.

Abstract: We report the observation of the metallic niobium migration within the molten Cu-Nb alloy mass on the synthesis of nano-granular Cuxwt\%Nb evolution, we prepared a series of granular samples by rapidly cooling a molten mixture of Cuxwt\%Nb, where the niobium concentration varied (x=3,5,15,20). Our main goal in this work was not only to establish a systematic, innovative and robust method to obtaining good quality samples, but also provide a clear recipe for obtaining similar systems to the investigations of their interesting physical properties. Beyond the understanding of the Cocoon Effect in Niobium-Copper alloys, we include a wide complementary elsewhere investigation into the very interesting and rich superconducting properties exhibited by the Niobium-Copper alloy. By employing a robust synthesis method, we successfully obtained samples characterized by well-defined spherical nano-precipitates of niobium, featuring regular sizes and grain spacing. Our study contributes not only to our understanding of the Niobium-Copper molten phase separation, micro-structure and the Cocoon Effect in these metallic alloys, but also sheds light on the intricate and important implications for the development and optimization of good quality granular metallic alloys for various applications. From our work, we obtained very impressive micro structural results, such as: $ d_{m} $ = 1.2 \mu, $ D_{m} $\le 2.2 \mu$m$ \ and \ \rho =1.785 \mu$m^{2}$, where $ d_{m} $ is the distance between Niobium grains, $D_{m}$ is the mean diameter of Niobium grains and \rho$ is the Niobium grain mean density in the Copper matrix.

Abstract: With their celebrated structural and chemical flexibility, perovskite oxides have served as a highly adaptable material platform for exploring emergent phenomena arising from the interplay between different degrees of freedom. Molecular dynamics (MD) simulations leveraging classical force fields, commonly depicted as parameterized analytical functions, have made significant contributions in elucidating the atomistic dynamics and structural properties of crystalline solids including perovskite oxides. However, the force fields currently available for solids are rather specific and offer limited transferability, making it time-consuming to use MD to study new materials systems since a new force field must be parameterized and tested first. The lack of a generalized force field applicable to a broad spectrum of solid materials hinders the facile deployment of MD in computer-aided materials discovery (CAMD). Here, by utilizing a deep-neural network with a self-attention scheme, we have developed a unified force field that enables MD simulations of perovskite oxides involving 14 metal elements and conceivably their solid solutions with arbitrary compositions. Notably, isobaric-isothermal ensemble MD simulations with this model potential accurately predict the experimental phase transition sequences for several markedly different ferroelectric oxides, including a 6-element ternary solid solution, Pb(In$_{1/2}$Nb$_{1/2}$)O$_3$--Pb(Mg$_{1/3}$Nb$_{2/3}$)O$_3$--PbTiO$_3$. We believe the universal interatomic potential along with the training database, proposed regression tests, and the auto-testing workflow, all released publicly, will pave the way for a systematic improvement and extension of a unified force field for solids, potentially heralding a new era in CAMD.

Abstract: The charge density wave (CDW) material 1T-TaS$_2$ exhibits a pulse-induced insulator-to-metal transition, which shows promise for next-generation electronics such as memristive memory and neuromorphic hardware. However, the rational design of TaS$_2$ devices is hindered by a poor understanding of the switching mechanism, the pulse-induced phase, and the influence of material defects. Here, we operate a 2-terminal TaS$_2$ device within a scanning transmission electron microscope (STEM) at cryogenic temperature, and directly visualize the changing CDW structure with nanoscale spatial resolution and down to 300 {\mu}s temporal resolution. We show that the pulse-induced transition is driven by Joule heating, and that the pulse-induced state corresponds to nearly commensurate and incommensurate CDW phases, depending on the applied voltage amplitude. With our in operando cryo-STEM experiments, we directly correlate the CDW structure with the device resistance, and show that dislocations significantly impact device performance. This work resolves fundamental questions of resistive switching in TaS$_2$ devices critical for engineering reliable and scalable TaS$_2$ electronics.

Abstract: Newly discovered altermagnets are magnetic materials exhibiting both compensated magnetic order, similar to antiferromagnets, and simultaneous non-relativistic spin-splitting of the bands, akin to ferromagnets. This characteristic arises from the specific symmetry operations that connect the spin sublattices. In this report, we show with ab initio calculations that the semiconductive MnSe exhibits altermagnetic spin-splitting in the wurtzite phase as well as a critical temperature well above room temperature. It is the first material from such space group identified to possess altermagnetic properties. Furthermore, we demonstrate experimentally through structural characterization techniques that it is possible to obtain thin films of both the intriguing wurtzite phase of MnSe and the more common rock-salt MnSe using molecular beam epitaxy on GaAs substrates. The choice of buffer layers plays a crucial role in determining the resulting phase and consequently extends the array of materials available for the physics of altermagnetism.

Abstract: 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 (ZB) 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 (CGCNN), Materials Graph Network (MEGNET), 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. Models are tested for different defect types as well as for defect charge transition levels. 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. 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.