Materials Science (cond-mat.mtrl-sci)

Abstract: Selenium has resurged as a promising photovoltaic material in solar cell research due to its wide direct bandgap of 1.95 eV, making it a suitable candidate for a top cell in tandem photovoltaic devices. However, the optoelectronic quality of selenium thin-films has been identified as a key bottleneck for realizing high-efficiency selenium solar cells. In this study, we present a novel approach for crystallizing selenium thin-films using laser-annealing as an alternative to the conventionally used thermal annealing strategy. By laser-annealing through a semitransparent substrate, a buried layer of high-quality selenium crystallites is formed and used as a growth template for solid-phase epitaxy. The resulting selenium thin-films feature larger and more preferentially oriented grains with a negligible surface roughness in comparison to thermally annealed selenium thin-films. We fabricate photovoltaic devices using this strategy, and demonstrate a record ideality factor of n=1.37, a record fill factor of FF=63.7%, and a power conversion efficiency of PCE=5.0%. The presented laser-annealing strategy is universally applicable and is a promising approach for crystallizing a wide range of photovoltaic materials where high temperatures are needed while maintaining a low substrate temperature.

Abstract: The respective unique merit of antiferromagnets and two-dimensional (2D) materials in spintronic applications inspire us to exploit 2D antiferromagnetic spintronics. However, the detection of the N\'eel vector in 2D antiferromagnets remains a great challenge because the measured signals usually decrease significantly in the 2D limit. Here we propose that the N\'eel vector of 2D antiferromagnets can be efficiently detected by the intrinsic nonlinear Hall (INH) effect which exhibits unexpected significant signals. As a specific example, we show that the INH conductivity of the monolayer manganese chalcogenides Mn$X$ ($X$=S, Se, Te) can reach the order of nm$\cdot$mA/V$^2$, which is orders of magnitude larger than experimental values of paradigmatic antiferromagnetic spintronic materials. The INH effect can be accurately controlled by shifting the chemical potential around the band edge, which is experimentally feasible via electric gating or charge doping. Moreover, we explicitly demonstrate its $2\pi$-periodic dependence on the N\'eel vector orientation based on an effective $k.p$ model. Our findings enable flexible design schemes and promising material platforms for spintronic memory device applications based on 2D antiferromagnets.

Abstract: Halide double perovskites are an emerging class of semiconductors with tremendous chemical and electronic diversity. While their bandstructure features can be understood from frontier-orbital models, chemical intuition for optical excitations remains incomplete. Here, we use \textit{ab initio} many-body perturbation theory within the $GW$ and the Bethe-Salpeter Equation approach to calculate excited-state properties of a representative range of Cs$_2$BB$'$Cl$_6$ double perovskites. Our calculations reveal that double perovskites with different combinations of B and B$'$ cations display a broad variety of electronic bandstructures and dielectric properties, and form excitons with binding energies ranging over several orders of magnitude. We correlate these properties with the orbital-induced anisotropy of charge-carrier effective masses and the long-range behavior of the dielectric function, by comparing with the canonical conditions of the Wannier-Mott model. Furthermore, we derive chemically intuitive rules for predicting the nature of excitons in halide double perovskites using electronic structure information obtained from computationally inexpensive DFT calculations.

Abstract: The bottom-up synthesis of carbon based nanomaterials directly on semiconductor surfaces allows to decouple their electronic and magnetic properties from the substrates. However, the lack of reactivity on these non-metallic surfaces hinders or reduces significantly the yield of these reactions. Such hurdles practically precludes transferring bottom-up synthesis strategies onto semiconducting and insulating surfaces. Here, we achieve a high polymerization yield of terphenyl molecules on the semiconductor TiO$_2$(110) surface by incorporating cobalt atoms as catalysts in the Ullmann coupling reaction. Cobalt atoms trigger the debromination of 4,4-dibromo-p-terphenyl (DBTP) molecules on TiO$_2$(110) and the formation of an intermediate organometallic phase already at room-temperature (RT). As the debromination temperature is drastically reduced, the homo-coupling temperature is also significantly lowered, preventing the desorption of DBTP molecules from the TiO$_2$(110) surface and leading to a radical improvement on the poly-para-phenylene (PPP) polymerization yield. The universality of this mechanism is demonstrated with an iodinated terphenyl derivative (DITP), which shows analogous dehalogenation and polymerization temperatures with a very similar reaction yield. Consequently, we propose to use minute amounts of metal catalyst to drive forward generic bottom-up synthesis strategies on non-metallic surfaces.

Abstract: The engineering of surface morphology and structure of the thin film is one of the essential technological assets for regulating the physical properties and functionalities of thin film-based devices. This study investigates the evolution of surface structure and magnetic anisotropy in epitaxially grown ultrathin Fe films on MgO (001) substrates subjected to multiple cycles of ion beam erosion (IBE) after growth. Ultrathin Fe film grows in 3D island mode and exhibits intrinsic fourfold magnetic anisotropy. After a few cycles of IBE, the film displays an induced uniaxial magnetic anisotropy that leads to a split in the hysteresis loop. In addition, clear and conclusive evidence of IBE mediated (2x2) reconstruction of the Fe surface has been observed. We also demonstrate that thermal annealing can reversibly tune the induced UMA and surface reconstruction. The feasibility of the IBE technique by properly selecting ion beam parameters for modification of surface structure has been highlighted apart from conventional methods of tailoring the morphology for tuning of UMA. Thus, the present work paves a way to explore the IBE-induced self-assembling phenomena further.

Abstract: Machine learning interatomic potentials (MLPs) are a promising technique for atomic modeling. While high accuracy and small errors are widely reported for MLPs, an open concern is whether MLPs can accurately reproduce atomistic dynamics and related physical properties in their applications in molecular dynamics (MD) simulations. In this study, we examine the current state-of-the-art MLPs and uncover a number of discrepancies related to atom dynamics, defects, and rare events (REs), in their MD simulations compared to ab initio methods. Our findings reveal that low averaged errors by current MLP testing are insufficient, leading us to develop novel quantitative metrics that better indicate the accurate prediction of related properties by MLPs in MD simulations. The MLPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the developed evaluation metrics, and the proposed process of developing such metrics are general to MLPs, thus providing valuable guidance for future testing, development, and improvements of accurate, robust, and reliable MLPs for atomistic modeling.

Abstract: The study of topological quantum materials for enhanced thermoelectric energy conversion has received significant attention recently. Topological materials (including topological insulators and Dirac/Weyl/nodal-line semi-metals) with unique nature of band structure involving linear and regular parabolic bands near Fermi level (E$_F$) have the potential to show promising TE properties. In this article, we report the promising TE performance of a quaternary chalcogenide (Cu$_2$ZnGeTe$_4$) having non-trivial topological phase. At ambient condition, the compound is a narrow band gap (0.067 eV) semiconductor, with a TE figure of merit (ZT) 1.2. Application of 5% strain drives the system to a topologically non-trivial Weyl semi-metal with the right combination of linear and parabolic bands near E$_F$, giving rise to a reasonable ZT of 0.36. Apart from strain, alloy engineering (Sn substituted at Ge) is also shown to induce topological non-triviality. The present work demonstrates the potential of such unique semimetals for exceptional electronic transport properties and hence excellent thermoelectric performance.

Abstract: We present a study of the self-interstitial point defect formation energies in silicon using a range of quantum chemical theories including the coupled cluster (CC) method within a periodic supercell approach. We study the formation energies of the X, T, H and C3V self-interstitials and the vacancy V. Our results are compared to findings obtained using different ab initio methods published in the literature and partly to experimental data. In order to achieve computational results that are converged with respect to system size and basis set, we employ the recently proposed finite size error corrections and basis set incompleteness error corrections. Our CCSD(T) calculations yield an order of stability of the X, H and T self-interstitials, which agrees both with quantum Monte Carlo results and with predictions obtained using the random-phase approximation as well as using screened hybrid functionals. Compared to quantum Monte Carlo results with backflow corrections, the CCSD(T) formation energies of X and H are only slightly larger by about 100 meV. However, in the case of the T self-interstitial, we find significant disagreement with all other theoretical predictions. Compared to quantum Monte Carlo calculations, CCSD(T) overestimates the formation energy of the T self-interstitial by 1.2 eV. Although this can partly be attributed to strong correlation effects, more accurate electronic structure theories are needed to understand these findings.

Abstract: Lack of rigorous reproducibility and validation are major hurdles for scientific development across many fields. Materials science in particular encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with both perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC) and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data-points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard

Abstract: The van der Waals (vdW) type-II multiferroic NiI$_2$ has emerged as a candidate for exploring non-collinear magnetism and magnetoelectric effects in the 2D limit. Frustrated intralayer exchange interactions on a triangular lattice result in a helimagnetic ground state, with spin-induced improper ferroelectricity stabilized by the interlayer interactions. Here we investigate the magnetic and structural phase transitions in bulk NiI$_2$, using high-pressure Raman spectroscopy, optical linear dichroism, and x-ray diffraction. We obtain evidence for a significant pressure enhancement of the antiferromagnetic and helimagnetic transition temperatures, at rates of $\sim15.3/14.4$ K/GPa, respectively. These enhancements are attributed to a cooperative effect of pressure-enhanced interlayer and third-nearest-neighbor intralayer exchange. These results reveal a general path for obtaining high-temperature type-II multiferroicity via high pressures in vdW materials.

Abstract: Synthesis of high-performance single crystal LiNi0.8Mn0.1Co0.1O2 (NMC811) in the absence of molten salt is challenging with no success yet. An innovative drop-in approach is discovered to synthesize single crystal NMC811 by controlling the morphology of transition metal hydroxide TM(OH)2 precursors followed by a simple decomposition step to form transition metal oxide (TMO) intermediates. Ni redistribution in TMO, as a result of the concurrent formation of mixed spinel and rock salt phases, helps deagglomerate the later formed NMC811 clusters of single crystals. As-prepared single crystal NMC811 is validated in a 2Ah pouch cell demonstrating 1000 stable cycling. The fundamentally new reaction mechanism of single crystal growth and segregation without molten salt provides a new direction towards cost-efficient manufacturing of single crystal NMC811 cathode for advanced lithium-based batteries.

Abstract: Neural networks are promising tools for high-throughput and accurate transmission electron microscopy (TEM) analysis of nanomaterials, but are known to generalize poorly on data that is "out-of-distribution" from their training data. Given the limited set of image features typically seen in high-resolution TEM imaging, it is unclear which images are considered out-of-distribution from others. Here, we investigate how the choice of metadata features in the training dataset influences neural network performance, focusing on the example task of nanoparticle segmentation. We train and validate neural networks across curated, experimentally-collected high-resolution TEM image datasets of nanoparticles under controlled imaging and material parameters, including magnification, dosage, nanoparticle diameter, and nanoparticle material. Overall, we find that our neural networks are not robust across microscope parameters, but do generalize across certain sample parameters. Additionally, data preprocessing heavily influences the generalizability of neural networks trained on nominally similar datasets. Our results highlight the need to understand how dataset features affect deployment of data-driven algorithms.

Abstract: A series of garnets of formula Er3+xGa5-xO12 is described, for which we report the crystal structures for both polycrystalline and single-crystal samples. The x limit in the garnet phase is between 0.5 and 0.6 under our conditions, with the Er fully occupying the normal garnet site plus half-occupying the octahedral site at x = 0.5 in place of the Ga normally present. Long-range antiferromagnetic order with spin ice-like frustration is suggested by the transition temperature (TN=0.8K) being much lower than the Curie-Weiss theta. The magnetic ordering temperature does not depend on the Er excess, but there is increasing residual entropy as the Er excess is increased, highlighting the potential for unusual magnetic behavior in this system.

Abstract: The biphenylene network (BPN) is a notable achievement in recent fabrication endeavors for conceiving new 2D materials. The stability of its boron nitride counterpart, BN-BPN, has been confirmed through numerical investigations. In this study, we conducted a density functional theory (DFT) analysis to examine the mechanical, electronic, thermodynamic, and optical properties of two other group-III counterparts of BPN: gallium nitride (BPN-GaN) and aluminum nitride (BPN-AlN). Our findings reveal that the band gap values for BPN-GaN and BPN-AlN are 2.3 eV and 3.2 eV, respectively, at the HSE06 level. At the GGA/PBE level, we found band gap values of 1.8 eV and 2.3 eV for BPN-GaN and BPN-AlN, respectively. Phonon calculations and ab initio molecular dynamics (AIMD) simulations suggest that BPN-AlN has good structural and dynamic stabilities. On the other hand, BPN-GaN displayed negative phonon frequencies, suggesting potential instability. Nevertheless, results from AIMD simulations point to its structural integrity with no bond reconstructions at 1000 K. These materials exhibit noteworthy UV activity, presenting promising prospects as UV collectors. The thermodynamic properties reveal that the heat capacity of both BPN-AlN and BPN-GaN increases with temperature, eventually reaching the Dulong-Petit limit at around 800 K. We also performed calculations to determine the elastic stiffness constants, Young's modulus, and Poisson ratio for both BPN-GaN and BPN-AlN, providing valuable insights into their mechanical properties.

Abstract: Niobium chloride (Nb3Cl8) is a layered 2D semiconducting material with many exotic properties including a breathing kagome lattice, a topological flat band in its band structure, and a crystal structure that undergoes a structural and magnetic phase transition at temperatures below 90 K. Despite being a remarkable material with fascinating new physics, the understanding of its phononic properties is at its infancy. In this study, we investigate the phonon dynamics of Nb3Cl8 in bulk and few layer flakes using polarized Raman spectroscopy and density-functional theory (DFT) analysis to determine the material's vibrational modes, as well as their symmetrical representations and atomic displacements. We experimentally resolved 12 phonon modes, 5 of which are A1g modes the remaining 7 are Eg modes, which is in strong agreement with our DFT calculation. Layer-dependent results suggest that the Raman peak positions are mostly insensitive to changes in layer thickness, while peak intensity and FWHM are affected. Raman measurements as a function of excitation wavelength (473, 532, 633, 785 nm) show a significant increase of the peak intensities when using a 473 nm excitation source, suggesting a near resonant condition. Low-temperature Raman measurements carried out at 7.6 K did not show any changes in the phonon modes and their symmetries, suggesting that the observed Raman modes may not be sensitive to the structural phase transition. Magneto-Raman measurements carried out at 140 and 2 K between -2 to 2 T show that Raman modes are not magnetically coupled. Overall, the study presented here significantly advances the fundamental understanding of the layered material Nb3Cl8 which can be further exploited for future applications.

Abstract: We propose a first-principles model of minimum lattice thermal conductivity ($\kappa_{\rm L}^{\rm min}$) based on a unified theoretical treatment of thermal transport in crystals and glasses. We apply this model to thousands of inorganic compounds and discover a universal behavior of $\kappa_{\rm L}^{\rm min}$ in crystals in the high-temperature limit: the isotropically averaged $\kappa_{\rm L}^{\rm min}$ is independent of structural complexity and bounded within a range from $\sim$0.1 to $\sim$2.6 W/[m$\cdot$K], in striking contrast to the conventional phonon gas model which predicts no lower bound. We unveil the underlying physics by showing that for a given parent compound $\kappa_{\rm L}^{\rm min}$ is bounded from below by a value that is approximately insensitive to disorder, but the relative importance of different heat transport channels (phonon gas versus diffuson) depends strongly on the degree of disorder. Moreover, we propose that the diffuson-dominated $\kappa_{\rm L}^{\rm min}$ in complex and disordered compounds might be effectively approximated by the phonon gas model for an ordered compound by averaging out disorder and applying phonon unfolding. With these insights, we further bridge the knowledge gap between our model and the well-known Cahill-Watson-Pohl (CWP) model, rationalizing the successes and limitations of the CWP model in the absence of heat transfer mediated by diffusons. Finally, we construct graph network and random forest machine learning models to extend our predictions to all compounds within the Inorganic Crystal Structure Database (ICSD), which were validated against thermoelectric materials possessing experimentally measured ultralow $\kappa_{\rm L}$. Our work offers a unified understanding of $\kappa_{\rm L}^{\rm min}$, which can guide the rational engineering of materials to achieve $\kappa_{\rm L}^{\rm min}$.