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At least 613 records · Page 34

Dual functionality of over-lithiated NMC for high energy silicon-based lithium-ion batteries

Owing to their high specific capacity and suitably low operating potential, silicon-based anodes are an attractive alternative to graphite in next-generation lithium-ion batteries. However, silicon anodes suffer from low initial coulombic efficiency and fast capacity decay, limiting their widespread application. Pre-lithiation strategies are highly appealing to compensate for irreversible active lithium loss and to boost the cell energy density. In this work, we maximize the cell energy density by direct pre-lithiation of the NMC (LiNi 0.5 Mn 0.3 Co 0.2 O 2 ) cathode to Li 1+x NMCO 2 without introducing inactive deadweight to either electrode. First, we demonstrate that Li 1+x NMCO 2 can be synthesized chemically, via reaction between NMC and lithium napthalide, and electrochemically. The NMC cathode is tolerant of a one-time over-lithiation up to 60 mA h g NMC -1 , giving capacity retention on par with untreated NMC in half cell electrochemical cycling. Using synchrotron X-ray absorption spectroscopy (ex situ) and diffraction (in situ), we demonstrate that higher amounts of over-lithiation lead to local structure distortion – driven by transition metal reduction to Jahn–Teller active Mn 3+ and Co 2+ – as well as bulk structural hysteresis during over-lithiation and layer “buckling” that increases the amount of lithium extracted from the structure in the charged state. The Li 1+x NMCO 2 with low-to-moderate over-lithiation capacity (23, 46, and 70 mA h g NMC -1 ) is proven to be a highly effective dual-purpose lithium source and cathode material in full cell tests with a commercially relevant Si–graphite anode. These cells show higher capacity, superior cycle life, and improved coulombic efficiencies when compared to those with stoichiometric NMC cathodes. Finally, this study introduces a new and simple method to pre-lithiate layered transition metal oxide cathodes, opening up new possibilities for the development of high energy density lithium-ion batteries with next-generation anodes.

25 ENERGY STORAGE↗

Demonstration of an x-ray Raman spectroscopy setup to study warm dense carbon at the high energy density instrument of European XFEL

We present a proof-of-principle study demonstrating x-ray Raman Spectroscopy (XRS) from carbon samples at ambient conditions in conjunction with other common diagnostics to study warm dense matter, performed at the high energy density scientific instrument of the European x-ray Free Electron Laser (European XFEL). We obtain sufficient spectral resolution to identify the local structure and chemical bonding of diamond and graphite samples, using highly annealed pyrolytic graphite spectrometers. Due to the high crystal reflectivity and XFEL brightness, we obtain signal strengths that will enable accurate XRS measurements in upcoming pump–probe experiments with a high repetition-rate, where the samples will be pumped with high-power lasers. Molecular dynamics simulations based on density functional theory together with XRS simulations demonstrate the potential of this technique and show predictions for high-energy-density conditions. Our setup allows simultaneous implementation of several different diagnostic methods to reduce ambiguities in the analysis of the experimental results, which, for warm dense matter, often relies on simplifying model assumptions. The promising capabilities demonstrated here provide unprecedented insights into chemical and structural dynamics in warm dense matter states of light elements, including conditions similar to the interiors of planets, low-mass stars, and other celestial bodies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Enhancing elastic properties of single element amorphous solids through long-range interactions

Elastic properties of amorphous solids remain a topic of intense interest due to their important roles in a wide range of applications. Prior works have focused on short-range, local structural features, such as number density and coordination number, to elucidate the underlying mechanism of elastic moduli in glasses. Here, we report strong correlations among the spatial extension of the interatomic potential, elastic moduli, and the coherence lengths of the medium-range structural order through molecular dynamics simulations for single element glasses. Our findings demonstrate an unconventional design principle to engineer elastic properties by extending the medium-range order and provide insights into the microscopic origin of elastic properties in amorphous solids.

36 MATERIALS SCIENCE↗

Density functional theory of water with the machine-learned DM21 functional

The delicate interplay between functional-driven and density-driven errors in density functional theory (DFT) has hindered traditional density functional approximations (DFAs) from providing an accurate description of water for over 30 years. Recently, the deep-learned DeepMind 21 (DM21) functional has been shown to overcome the limitations of traditional DFAs as it is free of delocalization error. To determine if DM21 can enable a molecular-level description of the physical properties of aqueous systems within Kohn–Sham DFT, we assess the accuracy of the DM21 functional for neutral, protonated, and deprotonated water clusters. Furthermore, we find that the ability of DM21 to accurately predict the energetics of aqueous clusters varies significantly with cluster size. Additionally, we introduce the many-body MB-DM21 potential derived from DM21 data within the many-body expansion of the energy and use it in simulations of liquid water as a function of temperature at ambient pressure. We find that size-dependent functional-driven errors identified in the analysis of the energetics of small clusters calculated with the DM21 functional result in the MB-DM21 potential systematically overestimating the hydrogen-bond strength and, consequently, predicting a more ice-like local structure of water at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Invariant discovery of features across multiple length scales: Applications in microscopy and autonomous materials characterization

Physical imaging is a foundational characterization method in areas from condensed matter physics and chemistry to astronomy and spans length scales from atomic to universe. Images encapsulate crucial data regarding atomic bonding, materials microstructures, and dynamic phenomena such as microstructural evolution and turbulence, among other phenomena. The challenge lies in effectively extracting and interpreting this information. Variational Autoencoders (VAEs) have emerged as powerful tools for identifying the underlying factors of variation in image data, providing a systematic approach to distilling meaningful patterns from complex data sets. However, a significant hurdle in their application is the definition and selection of appropriate descriptors reflecting local structures. Here, we introduce the scale-invariant VAE approach (SI-VAE) based on the progressive training of the VAE with the descriptors sampled at different length scales. The SI-VAE allows the discovery of the length scale-dependent factors of variation in the system. Here, we illustrate this approach using the ferroelectric domain images and generalize it to the movies of the electron-beam induced phenomena in graphene and topography evolution across combinatorial libraries. This approach can further be used to initialize the decision making in automated experiments including structure–property discovery and can be applied across a broad range of imaging methods. This approach is universal and can be applied to any spatially resolved data including both experimental imaging studies and simulations, and can be particularly useful for exploration of phenomena such as turbulence and scale-invariant transformation fronts.

36 MATERIALS SCIENCE↗

Improved loss functions for machine-learned atomic potentials

Machine learning (ML) has become an invaluable tool across a wide array of domains in science as researchers find new ways to leverage its predictive power. This is especially true in chemistry, where ML is used to fit chemical properties or desirable attributes to the local structure of molecules and materials. In the pursuit of greater accuracy, it is relatively simple to increase the size or complexity of such models, although this often requires simultaneously seeking larger datasets in order to both fit and interpret the larger number of parameters. However, it is equally important to assess the quality and relative importance of the data and how these factors impact the training process. We, therefore, investigate the impact of using different loss functions for training neural network potentials (NNPs), as the loss function defines the error and parameter gradients used to train the NNP. In particular, we test the mean-squared error and Huber loss functions and, using insight from these functions, derive a new loss function based on the Asinh function, which yields significant improvement in the accuracy and generality of NNPs. We show that by discounting/minimizing errors and anomalies in the optimization process, both the Huber and Asinh loss functions improve the training of NNPs, leading to a final potential with a greater effective dimensionality.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Elucidation of the physical factors that control activated transport of penetrants in chemically complex glass-forming liquids

Understanding the activated transport of penetrant or tracer atoms and molecules in condensed phases is a challenging problem in chemistry, materials science, physics, and biophysics. Many angstrom- and nanometer-scale features enter due to the highly variable shape, size, interaction, and conformational flexibility of the penetrant and matrix species, leading to a dramatic diversity of penetrant dynamics. Based on a minimalist model of a spherical penetrant in equilibrated dense matrices of hard spheres, a recent microscopic theory that relates hopping transport to local structure has predicted a novel correlation between penetrant diffusivity and the matrix thermodynamic dimensionless compressibility, S 0 ( T ) (which also quantifies the amplitude of long wavelength density fluctuations), as a consequence of a fundamental statistical mechanical relationship between structure and thermodynamics. Moreover, the penetrant activation barrier is predicted to have a factorized/multiplicative form, scaling as the product of an inverse power law of S 0 ( T ) and a linear/logarithmic function of the penetrant-to-matrix size ratio. This implies an enormous reduction in chemical complexity that is verified based solely on experimental data for diverse classes of chemically complex penetrants dissolved in molecular and polymeric liquids over a wide range of temperatures down to the kinetic glass transition. The predicted corollary that the penetrant diffusion constant decreases exponentially with inverse temperature raised to an exponent determined solely by how S 0 ( T ) decreases with cooling is also verified experimentally. Our findings are relevant to fundamental questions in glassy dynamics, self-averaging of angstrom-scale chemical features, and applications such as membrane separations, barrier coatings, drug delivery, and self-healing.

Science & Technology - Other Topics↗

Powers of magnetic graph matrix: Fourier spectrum, walk compression, and applications

Magnetic graphs, originally developed to model quantum systems under magnetic fields, have recently emerged as a powerful framework for analyzing complex directed networks. Existing research has primarily used the spectral properties of the magnetic graph matrix to study global and stationary network features. However, their capacity to model local, nonequilibrium behaviors, often described by matrix powers, remains largely unexplored. We present a combinatorial interpretation of the magnetic graph matrix powers through directed walk profiles—counts of graph walks indexed by the number of edge reversals. Crucially, we establish that walk profiles correspond to a Fourier transform of magnetic matrix powers. The connection allows exact reconstruction of walk profiles from magnetic matrix powers at multiple discrete potentials, and more importantly, an even smaller number of potentials often suffices for accurate approximate reconstruction in real networks. This shows the empirical compressibility of the information captured by the magnetic matrix. This fresh perspective suggests further applications; for example, we illustrate how powers of the magnetic matrix can identify frustrated directed cycles (e.g., feedforward loops) and can be effectively employed for link prediction by encoding local structural details in directed graphs.

complex networks↗

Exploring few and single layer CrPS 4 with near-field infrared spectroscopy

Abstract We combine synchrotron-based near-field infrared spectroscopy and first principles lattice dynamics calculations to explore the vibrational response of CrPS 4 in bulk, few-, and single-layer form. Analysis of the mode pattern reveals a C 2 polar + chiral space group, no symmetry crossover as a function of layer number, and a series of non-monotonic frequency shifts in which modes with significant intralayer character harden on approach to the ultra-thin limit whereas those containing interlayer motion or more complicated displacement patterns soften and show inflection points or steps. This is different from MnPS 3 where phonons shift as 1/size 2 and are sensitive to the three-fold rotation about the metal center that drives the symmetry crossover. We discuss these differences as well as implications for properties such as electric polarization in terms of presence or absence of the P–P dimer and other aspects of local structure, sheet density, and size of the van der Waals gap.

36 MATERIALS SCIENCE↗

Block encoding of the three-dimensional heterogeneous Poisson equation with application to fracture flow

Quantum linear system (QLS) algorithms offer the potential to solve large-scale linear systems exponentially faster than classical methods. However, applying QLS algorithms to real-world problems remains challenging due to issues such as state preparation, data loading, and efficient information extraction. In this work, we study the feasibility of applying QLS algorithms to solve discretized three-dimensional (3D) heterogeneous Poisson equations, with specific examples relating to groundwater flow through geologic fracture networks. We explicitly construct a block encoding for the 3D heterogeneous Poisson matrix by leveraging the sparse local structure of the discretized operator. While classical solvers benefit from preconditioning, we show that block encoding the system matrix and preconditioner separately does not improve the effective condition number that dominates the QLS run-time. This differs from classical approaches where the preconditioner and the system matrix can often be implemented independently. Nevertheless, due to the structure of the problem in three dimensions, the quantum algorithm achieves a run-time of 𝑂⁡(𝑁 2/3 polylog 𝑁 ⋅log (1/𝜖)), outperforming the best classical methods (with run times of 𝑂⁡(𝑁⁢log 𝑁 ⋅log (1/𝜖))) and offering exponential memory savings. These results highlight both the promise and limitations of QLS algorithms for practical scientific computing, and point to effective condition-number reduction as a key barrier in achieving quantum advantages.

58 GEOSCIENCES↗

Seamlessly joining length scales: From atomistic thermal graphs to anisotropic continuum conductivity

Thermal transport in complex solids is governed by local structure, defects, and anisotropy, yet most continuum models still rely on oversimplified and homogenized conductivities. Here, we bridge atomistic and continuum descriptions by building finite element (FE) models directly from the site-projected thermal conductivity (SPTC), an atomic-level decomposition of the Green–Kubo thermal conductivity. We introduce a toolkit, the “Simulator Collection for Atomic-to-Continuum Scales (SCACS)”, which uses a graph neural network to predict SPTC on large atomic structures, coarse-grains these fields into anisotropic conductivity tensors, and embeds them into the heat-flow FE equation with a customized, anisotropy-aware adaptive mesh refinement scheme. Applied to silicon nanostructures, the resulting FE models act as representative volume elements, reproduce bulk conductivities, and capture interfacial and defect-driven anisotropy while maintaining thermodynamic consistency. Additionally, SCACS predicts experimental conductance trends and fields. This work demonstrates a general route for transferring atomistic transport information into device-scale thermal simulations with physics-based approximations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Pseudospin-lattice coupling and electric control of the square-lattice iridate Sr2IrO4

Sr 2 IrO 4 is an archetypal spin-orbit-coupled Mott insulator and has been extensively studied in part because of a wide range of predicted states. Limited experimental characterization of these states thus far brings to light the extraordinary susceptibility of the physical properties to the lattice, particularly, the Ir-O-Ir bond angle. Here, we report a microscopic rotation of the IrO 6 octahedra below 50 K measured by single crystal neutron diffraction. Overall, this sharp lattice anomaly provides keys to understanding the anomalous low-temperature physics and a direct confirmation of a crucial role that the Ir-O-Ir bond angle plays in determining the ground state. Indeed, as also demonstrated in this study, applied electric current readily weakens the antiferromagnetic order via the straightening of the Ir-O-Ir bond angle, highlighting that even slight change in the local structure can disproportionately affect the physical properties in the spin-orbit-coupled system.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Vibrational properties of CuInP 2 S 6 across the ferroelectric transition

In order to explore the properties of a two-sublattice ferroelectric, we measured the infrared and Raman scattering response of CuInP 2 S 6 across the ferroelectric and glassy transitions and compared our findings to a symmetry analysis, calculations of phase stability, and lattice dynamics. In addition to uncovering displacive character and a large hysteresis region surrounding the ferroelectric transition temperature T C , we identify the vibrational modes that stabilize the polar phase and confirm the presence of two ferroelectric variants with opposite polarizations. Below T C , a poorly understood relaxational or glassy transition at T g is characterized by local structure changes in the form of subtle peak shifting and activation of low frequency out-of-plane Cu- and In-containing modes. Here, the latter are due to changes in the Cu/In coordination environments and associated order-disorder processes. Moreover, T g takes place in two steps with another large hysteresis region and significant underlying scattering. Combined with imaging of the room temperature phase separation, this effort lays the groundwork for studying CuInP 2 S 6 under external stimuli and in the ultrathin limit.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Nanoscale visualization of the thermally driven evolution of antiferromagnetic domains in FeTe thin films

Antiferromagnetic order, being a ground state of a number of exotic quantum materials, is of immense interest both from the fundamental physics perspective and for driving potential technological applications. For a complete understanding of antiferromagnetism in materials, nanoscale visualization of antiferromagnetic domains, domain walls, and their robustness to external perturbations is highly desirable. Here, we synthesize antiferromagnetic FeTe thin films using molecular-beam epitaxy. We visualize local antiferromagnetic ordering and domain formation using spin-polarized scanning tunneling microscopy. From the atomically resolved scanning tunneling microscopy topographs, we calculate local structural distortions to find a high correlation with the distribution of the antiferromagnetic order. This is consistent with the monoclinic structure in the antiferromagnetic state. Interestingly, we observe a substantial domain-wall change by small temperature variations, unexpected for the low-temperature changes used compared to the much higher antiferromagnetic ordering temperature of FeTe. This is in contrast to electronic nematic domains in the cousin FeSe multilayer films, where we find no electronic or structural change within the same temperature range. Further, our experiments provide the atomic-scale imaging of perturbation-driven magnetic domain evolution simultaneous with the ensuing structural response of the system. The results reveal surprising thermally driven modulations of antiferromagnetic domains in FeTe thin films well below the Néel temperature.

36 MATERIALS SCIENCE↗

Real-space visualization of atomic displacements in a long-wavelength charge density wave using cryogenic 4D-STEM

EuA⁢l 4 , a known skyrmion magnet, represents a class of materials that show complex electronic behaviors involving long-wavelength charge density wave (CDW). Here we realize direct real-space imaging of the concomitant periodic lattice distortion (PLD) with picometer precision in EuA⁢l 4 . Simultaneous imaging of microstructure, polarization, and interatomic distance is made possible using cryogenic four-dimensional scanning transmitting electron microscopy (cryo-4DSTEM). With this approach, we determined that the PLD in EuA⁢l 4 incorporates two out-of-phase atomic displacement modes on the Eu and Al sublattices. The PLD introduces periodic local symmetry fluctuations from the average symmetry of the bulk EuA⁢l 4 crystal. This critical local structural insight helps understanding of charge-spin-lattice couplings behind the intertwined quantum order in EuA⁢l 4 . Furthermore, our results also demonstrate cryo-4DSTEM as an indispensable approach for probing CDW and related quantum materials at low temperatures.

Ni, Haoyang↗

Generalized approach for rapid entropy calculation of liquids and solids

We build a comprehensive methodology for the fast computation of entropy across both solid and liquid phases. The proposed method utilizes a single trajectory of molecular dynamics (MD) to facilitate the calculation of entropy, which is composed of three components. The electronic entropy is determined through the temporal average acquired from density functional theory MD simulations. The vibrational entropy, typically the predominant contributor to the total entropy, even within the liquid state, is evaluated by computing the phonon density of states via the velocity autocorrelation function. The most arduous component to quantify, the configurational entropy, is assessed by probability analysis of the local structural arrangement and atomic distribution. We illustrate, through a variety of examples, that this method is both a versatile and valid technique for characterizing the thermodynamic states of both solids and liquids. Furthermore, this method is employed to expedite the calculation of melting temperatures, demonstrating its practical utility in computational thermodynamics.

36 MATERIALS SCIENCE↗

Unraveling Li growth kinetics in solid electrolytes due to electron beam charging

Revealing the local structure of solid electrolytes (SEs) with electron microscopy is critical for the fundamental understanding of the performance of solid-state batteries (SSBs). However, the intrinsic structural information in the SSB can be misleading if the sample’s interactions with the electron beams are not fully understood. In this work, we systematically investigate the effect of electron beams on Al-doped lithium lanthanum zirconium oxide (LLZO) under different imaging conditions. Li metal is observed to grow directly on the clean surface of LLZO. The Li metal growth kinetics and the morphology obtained are found to be heavily influenced by the temperature, accelerating voltage, and electron beam intensity. We prove that the lithium growth is due to the LLZO delithiation activated by a positive charging effect under electron beam emission. Our results deepen the understanding of the electron beam impact on SEs and provide guidance for battery material characterization using electron microscopy.

36 MATERIALS SCIENCE↗

Reactive high-spin iron(IV)-oxo sites through dioxygen activation in a metal–organic framework

In nature, nonheme iron enzymes use dioxygen to generate high-spin iron(IV)=O species for a variety of oxygenation reactions. Although synthetic chemists have long sought to mimic this reactivity, the enzyme-like activation of O 2 to form high-spin iron(IV) = O species remains an unrealized goal. Here, we report a metal–organic framework featuring iron(II) sites with a local structure similar to that in α-ketoglutarate-dependent dioxygenases. The framework reacts with O 2 at low temperatures to form high-spin iron(IV) = O species that are characterized using in situ diffuse reflectance infrared Fourier transform, in situ and variable-field Mössbauer, Fe Kβ x-ray emission, and nuclear resonance vibrational spectroscopies. In the presence of O 2 , the framework is competent for catalytic oxygenation of cyclohexane and the stoichiometric conversion of ethane to ethanol.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗