Materials Science (cond-mat.mtrl-sci)

Abstract: High degree of fluorination for ether electrolytes has resulted in improved cycling stability of lithium metal batteries (LMBs) due to stable SEI formation and good oxidative stability. However, the sluggish ion transport and environmental concerns of high fluorination degree drives the need to develop less fluorinated structures. Here, we introduce bis(2-fluoroethoxy)methane (F2DEM) which features monofluorination of the acetal backbone. High coulombic efficiency (CE) and stable long-term cycling in Li||Cu half cells can be achieved with F2DEM even under fast Li metal plating conditions. The performance of F2DEM is further compared with diethoxymethane (DEM) and 2-[2-(2,2-Difluoroethoxy)ethoxy]-1,1,1-Trifluoroethane (F5DEE). The structural similarity of DEM allows us to better probe the effects of monofluorination, while F5DEE is chosen as the one of the best performing LMB electrolytes for reference. The monofluorine substitution provides improved oxidation stability compared to non-fluorinated DEM, as demonstrated in the linear sweep voltammetry (LSV) and voltage holding experiments in Li||Pt and Li||Al cells. Higher ionic conductivity compared to F5DEE is also observed due to the decreased degree of fluorination. Furthermore, 1.75 M lithium bis(fluorosulfonyl)imide (LiFSI) / F2DEM displays significantly lower overpotential compared with the two reference electrolytes, which improves energy efficiency and enables its application in high-rate conditions. Comparative studies of F2DEM with DEM and F5DEE in anode-free (LiFePO4) LFP pouch cells and high-loading LFP coin cells with 20 {\mu}m excess Li further show improved capacity retention of F2DEM electrolyte.

Abstract: The negative thermal expansion (NTE) material is counterintuitive due to its typical feature of volume contraction on heating, which can act as the thermal-expansion compensators to counteract the normal positive thermal expansion. A wide temperature range of NTE behavior is desired, whereas the performance optimization by traditional doping strategy has reached its upper limit. In this paper, the unique sluggish characteristic in high entropy materials is proposed to broaden the NTE temperature range by relaxing the sharp phase transition in Mn-based anti-perovskite nitride. We propose an empirical screening method to synthesis the high-entropy anti-perovskite (HEAP). A remarkable NTE behavior (up to {\Delta}T = 235 K, 5 K < T < 240 K) with the coefficient of thermal expansion (CTE) of - 4.7 ppm/K has been observed in typical HEAP Mn3Cu0.2Zn0.2Ga0.2Ge0.2Mn0.2N, whose working temperature range is far wider than that of traditional low-entropy doping system. The wide temperature range of phase separation due to sluggish phase transition is responsible for the broadened NTE behavior in HEAP. Our demonstration provides a unique paradigm in the broadening of the NTE temperature range for phase transition induced NTE materials through entropy engineering.

Abstract: This research demonstrates that Ising machines can effectively solve optimal elemental configuration searches in crystals, with Au-Cu alloys serving as an example. The energy function is derived using the cluster expansion method in the form of a QUBO function, enabling efficient problem-solving via Ising machines. We have successfully obtained reasonable solutions for crystal structures consisting of over 10,000 atoms. Notably, we have also obtained plausible solutions for optimization problems with constrained solutions, such as situations where the composition ratio of atomic species is predetermined. These findings suggest that Ising machines can be valuable tools for addressing materials science challenges.

Abstract: Kohn-Sham density functional theory (DFT) is nowadays widely used for electronic structure theory simulations, and the accuracy and efficiency of DFT rely on approximations of the exchange-correlation functional. By inclusion of the kinetic energy density $\tau$, the meta-generalized-gradient approximation (meta-GGA) family of functionals achieves better accuracy and flexibility while retaining the efficiency of semi-local functionals. The SCAN meta-GGA functional has been proven to yield accurate results for solid and molecular systems. We implement meta-GGA functionals with both numerical atomic orbitals and plane wave basis in the ABACUS package. Apart from the exchange-correlation potential, we also discuss the evaluation of force and stress. To validate our implementation, we perform finite-difference tests and convergence tests with the SCAN meta-GGA functional. We further test water hexamers, weakly interacting molecules of the S22 dataset, as well as 13 semiconductors. The results show satisfactory agreements with previous calculations and available experimental values.

Abstract: A plethora of two-dimensional (2D) materials entered the physics and engineering scene in the last two decades. Their robust, membrane-like sheet permit -- mostly require -- deposition, giving rise to solid-solid dry interfaces whose bodily mobility, pinning, and general tribological properties under shear stress are currently being understood and controlled, experimentally and theoretically. In this Colloquium we use simulation case studies of twisted graphene system as a prototype workhorse tool to demonstrate and discuss the general picture of 2D material interface sliding. First, we highlight the crucial mechanical difference, often overlooked, between small and large incommensurabilities, corresponding e.g., to small and large twist angles in graphene interfaces. In both cases, focusing on flat, structurally lubric, "superlubric" geometries, we elucidate and review the generally separate scaling with area of static friction in pinned states and of kinetic friction during sliding, tangled as they are with the effects of velocity, temperature, load, and defects. Including the role of island boundaries and of elasticity, and corroborating when possible the existing case-by-case results in literature beyond graphene, the overall picture proposed is meant for general 2D material interfaces, that are of importance for the physics and technology of existing and future bilayer and multilayer systems.

Abstract: Despite symmetrical polarization, the magnitude of a light-induced voltage is known to be asymmetric with respect to poling sign in many photovoltaic (PV) ferroelectrics (FEs). This asymmetry remains unclear and is often attributed to extrinsic effects. We show here for the first time that such an asymmetry can be intrinsic, steaming from the superposition of asymmetries of internal FE bias and electro-piezo-strictive deformation. This hypothesis is confirmed by the observed decrease of PV asymmetry for smaller FE bias. Moreover, the both PV effect and remanent polarization are found to increase under vacuum-induced expansion and to decrease for gas-induced compression, with tens percents tunability. The change in cations positions under pressure is analysed through the first-principle density functional theory calculations. The reported properties provide key insight for FE-based solar elements optimization.

Abstract: Ni thin films grown by thermal evaporation and sputtering under different deposition conditions are characterized for structural and morphological properties using X-ray diffraction (XRD) and atomic force microscopy (AFM) techniques. XRD results suggested the growth of polycrystalline fcc Ni phase for all the samples. Morphological characteristics of the films were compared by analysing AFM data for root mean square roughness, height-height correlation function and power spectral density (PSD) measurements. Applying fractal and k-correlation fitting models to the PSD data, different morphological parameters are quantified. The study suggested that Ni films grown at higher substrate temperature (~ 150 oC) by thermal evaporation and at low Ar pressure (~ 0.2 Pa) by sputtering techniques yielded films of small surface roughness with Brownian fractal self-affine surfaces.

Abstract: MXenes are a family of 2D transition metal carbides and nitrides with remarkable properties and great potential for energy storage and catalysis applications. However, their oxidation behavior is not yet fully understood, and there are still open questions regarding the spatial distribution and precise quantification of surface terminations, intercalated ions, and possible uncontrolled impurities incorporated during synthesis and processing. Here, atom probe tomography analysis of as-synthesized Ti3C2Tx MXenes reveals the presence of alkali (Li, Na) and halogen (Cl, F) elements as well as unetched Al. Following oxidation of the colloidal solution of MXenes, it is observed that the alkalies enriched in TiO2 nanowires. Although these elements are tolerated through the incorporation by wet chemical synthesis, they are often overlooked when the activity of these materials is considered, particularly during catalytic testing. This work demonstrates how the capability of atom probe tomography to image these elements in 3D at the near-atomic scale can help to better understand the activity and degradation of MXenes, in order to guide their synthesis for superior functional properties.

Abstract: Recent advances in multistable metamaterials reveal a link between structural configuration transition and Boolean logic, heralding a new generation of computationally capable intelligent materials. To enable higher-level computation, existing computational frameworks require the integration of large-scale networked logic gates, which places demanding requirements on the fabrication of materials counterparts and the propagation of signals. Inspired by cellular automata, we propose a novel computational framework based on multistable origami metamaterials by incorporating reservoir computing, which can accomplish high-level computation tasks without the need to construct a logic gate network. This approach thus eleimates the demanding requirements for fabrication of materials and signal propagation when constructing large-scale networks for high-level computation in conventional mechano-logic. Using the multistable stacked Miura-origami metamaterial as a validation platform, digit recognition is successfully implemented through experiments by a single actuator. Moreover, complex tasks, such as handwriting recognition and 5-bit memory tasks, are also shown to be feasible with the new computation framework. Our research represents a significant advancement in developing a new generation of intelligent materials with advanced computational capabilities. With continued research and development, these materials could have a transformative impact on a wide range of fields, from computational science to material mechano-intelligence technology and beyond.

Abstract: The discovery of new functional materials is one of the key challenges in materials science. Combinatorial high-throughput approaches using reactive sputtering are commonly employed to screen unexplored phase spaces. During reactive combinatorial deposition the process conditions are rarely optimized, which can lead to poor crystallinity of the thin films. In addition, sputtering at shallow deposition angles can lead to off-axis preferential orientation of the grains. This can make the results from a conventional structural phase screening ambiguous. Here we perform a combinatorial screening of the Zn-Ta-N phase space with the aim to synthesize the novel semiconductor Zn2TaN3. While the results of the XRD phase screening are inconclusive, including chemical state analysis mapping in our workflow allows us to see a very clear discontinuity in the evolution of the Ta binding environment. This is indicative of the formation of a new ternary phase. In additional experiments, we isolate the material and perform a detailed characterization confirming the formation of single phase WZ-Zn2TaN3. Besides the formation of the new ternary nitride, we map the functional properties of ZnxTa1-xN and report previously unreported clean chemical state analysis for Zn3N2, TaN and Zn2TaN3. Overall, the results of this study showcase common challenges in high-throughput materials screening and highlight the merit of employing characterization techniques sensitive towards changes in the materials' short-range order and chemical state.

Abstract: The thermal transport across inorganic/organic interfaces attracts interest for both academic and industry due to its widely applications in flexible electronics etc. Here, the interfacial thermal conductance of inorganic/organic interfaces consisting of silicon and polyvinylidene fluoride is systematically investigated by molecular dynamics simulations. Interestingly, it is demonstrated that a modified silicon surface with hydroxyl groups can drastically enhance the conductance by 698%. These results are elucidated based on interfacial couplings and lattice dynamics insights. This study not only provides feasible strategies to effectively modulate the interfacial thermal conductance of inorganic/organic interfaces but also deepens the understanding of the fundamental physics underlying phonon transport across interfaces.

Abstract: The resulting microstructure after the sintering process determines many materials properties of interest. In order to understand the microstructural evolution, simulations are often employed. One such simulation method is the phase-field method, which has garnered much interest in recent decades. However, the method lacks a complete model for sintering, as previous works could show unphysical effects and the inability to reach representative volume elements. Thus the present paper aims to close this gap by employing molecular dynamics and determining rules of motion which can be translated to a phase-field model. The resulting phase-field model is shown to be representative starting from particle counts between 97 and 262 and contains the qualitative correct dependence of sintering rate on particle size.

Abstract: Green hydrogen production is crucial for a sustainable future, but current catalysts for the oxygen evolution reaction (OER) suffer from slow kinetics, despite many efforts to produce optimal designs, particularly through the calculation of descriptors for activity. In this study, we develop a dataset of density functional theory calculations of bulk and surface perovskite oxides, and adsorption energies of OER intermediates, which includes compositions up to quaternary and facets up to (555). We demonstrate that per-site properties of perovskite oxides such as Bader charge or band center can be tuned through element substitution and faceting, and develop a machine learning model that accurately predicts these properties directly from the local chemical environment. We leverage these per-site properties to identify promising perovskites with high theoretical OER activity. The identified design principles and promising new materials provide a roadmap for closing the gap between current artificial catalysts and biological enzymes.

Abstract: Superhard materials with good fracture toughness have found wide industrial applications, which necessitates the development of accurate hardness and fracture toughness models for efficient materials design. Although several macroscopic models have been proposed, they are mostly semiempirical based on prior knowledge or assumptions, and obtained by fitting limited experimental data. Here, through an unbiased and explanatory symbolic regression technique, we built a macroscopic hardness model and fracture toughness model, which only require shear and bulk moduli as inputs. The developed hardness model was trained on an extended dataset, which not only includes cubic systems, but also contains non-cubic systems with anisotropic elastic properties. The obtained models turned out to be simple, accurate, and transferable. Moreover, we assessed the performance of three popular deep learning models for predicting bulk and shear moduli, and found that the crystal graph convolutional neural network and crystal explainable property predictor perform almost equally well, both better than the atomistic line graph neural network. By combining the machine-learned bulk and shear moduli with the hardness and fracture toughness prediction models, potential superhard materials with good fracture toughness can be efficiently screened out through high-throughput calculations.