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Lithium-graphite intercalation compounds (Li-GICs) are by far the most common anode material for modern Li-ion batteries. However, the dielectric response of this material in the electrostatic limit and its variation depending on the state of charge (SOC) has not been investigated to a satisfactory degree - neither by means of theory nor by experiment - and especially not for the higher range of SOC. In this work, we - for the first time - predict a mostly linear dependency of the relative permittivity εr on the SOC, from ≈ 7 at SOC 0% to ≈ 25 at SOC 100%. This is achieved by making use of our recently published DFTB parametrization for Li-GICs based on a machine-learned repulsive potential in order to overcome the computational hurdles of sampling the long-ranged Coulomb interactions within this material. In doing so, we provide novel insight into a property which is highly desired, particularly as an input parameter for charged kinetic Monte Carlo simulations.

It is of general interest to combine the faradaic processes based high energy density of a battery with the non-faradaic processes based high power density of a capacitor in one cell. Surface area and functional groups of electrode materials strongly affect these properties, but a mechanistic explanation for the interplay between such process and how to balance them is largely missing. For the anode material Li4Ti5O12 (LTO), we suggest a polaron based mechanism that influences Li ion uptake and mobility. Electrolytes containing a lithium salt induce an observable change in the bulk NMR relaxation properties of LTO nano particles. The longitudinal 7Li NMR relaxation time of bulk LTO can change by almost an order of magnitude and, therefore, reacts very sensitively to the cation and its concentration in the surrounding electrolyte. The reversible effect is largely independent of the used anions and of potential anion decomposition products. It is concluded that lithium salt containing electrolytes increase the mobility of surface polarons. These polarons and additional lithium cations from the electrolyte can now diffuse through the bulk, induce the observed enhanced relaxation rate and enable the non-faradaic process. This picture of a Li+ ion equilibrium between electrolyte and solid may help with improving the charging properties of electrode materials.

ChemCatChem : the European Society Journal for Catalysis 14(19), e202200300 (2022). doi:10.1002/cctc.202200300 special issue: "Catalysts and Reactors under Dynamic Conditions for Energy Storage and Conversion", Published by Wiley-VCH, Weinheim

Detrimental to the performance of solid oxide cells (SOCs) is the interface between electrolyte and each electrode: for purely electron-conducting electrodes, it is part of the triple phase boundary where the reactions take place, and for mixed ionic and electronic conductor (MIEC) electrodes, the oxygen ions coming from or moving into the electrolyte are transported through this phase boundary. As such, the performance of SOFCs and SOECs is strongly dependent on the chemical nature of this interface. By combining state-of-the-art electron microscopy, synchrotron-based X-ray spectroscopy and theoretical calculations, we were able to obtain an atomically resolved picture of the interface between yttria-stabilized zirconia (YSZ, with 8 mol% Y2O3) and lanthanum strontium manganite (LSM, (La0.8Sr0.2)0.95MnO3-δ).[1] Energy-dispersive X-ray (EDX) spectroscopy measurements in the transmission electron microscope (TEM) reveal the presence of a significant amount of inter-diffusion between YSZ and LSM. Only strontium does not show any diffusion into YSZ, resulting in a shift of its intensity distribution profile of approximately 0.8 nm with respect to all other elements. All this is in agreement with Monte-Carlo-based simulations that were used to theoretically model the YSZ/LSM boundary including the ion- as well as site-specific swapping probabilities for the diffusion process.[1] According to these simulations, this diffusion region is slightly amorphous. By means of atomically resolved scanning TEM (STEM), this reduction of long-range order was observed experimentally for an approximately 1.5 nm wide slab on the YSZ side of the boundary, clearly distinguishing it from the bulk fluorite structure. In materials science, distinct ‘2D-like’ layers at grain boundaries and interfaces are often referred to as ’complexions’. While these can in general occur in different types, depending on thickness and retained order,[2] they are all characterized by a self-limited (finite) width and thermodynamic stability obtained only by confinement in between two bulk phases (i.e. they cannot exist on their own without neighboring phases).[2] These complexions have recently also been discovered in other energy-related systems such as battery materials.[3] The presence of such a stable complexion might be the reason why the YSZ/LSM interface is not as prone to the formation of lanthanum or strontium zirconates as other perovskites such as lanthanum strontium cobalt ferrite (LSCF).[4] Consequently, it might have significant influence on the chemical stability of such SOCs and a thorough understanding of the complexions between electrolyte and electrode may in the future allow fine tuning of SOC performance and stability. This requires the determination of electrochemical and thermodynamic properties of these interface complexions for which we will, in a next step, employ theoretical simulations and experimental techniques in order to find out how different transport phenomena, widths and stabilities of the layers behave with temperature, time and chemical environment. [1] H. Tuerk, F.-P. Schmidt, T. Götsch et al. in preparation [2] S. J. Dillon et al. Acta Mater. 2007, 55, 6208–6218 [3] J. Timmermann et al. Phys. Rev. Lett. 2020, 125, 206101 [4] W. Wang et al. J. Electrochem. Soc. 2006, 153, A2066

Compound materials, such as transition-metal (TM) carbides, are anticipated to be effective electrocatalysts for the carbon dioxide reduction reaction (CO2RR) to useful chemicals. This expectation is nurtured by density functional theory (DFT) predictions of a break of key adsorption energy scaling relations that limit CO2RR at parent TMs. Here, we evaluate these prospects for hexagonal Mo2C in aqueous electrolytes in a multimethod experiment and theory approach. We find that surface oxide formation completely suppresses the CO2 activation. The oxides are stable down to potentials as low as −1.9 V versus the standard hydrogen electrode, and solely the hydrogen evolution reaction (HER) is found to be active. This generally points to the absolute imperative of recognizing the true interface establishing under operando conditions in computational screening of catalyst materials. When protected from ambient air and used in nonaqueous electrolyte, Mo2C indeed shows CO2RR activity.

Using first-principles density-functional theory calculations combined with ab initio thermodynamics, we introduce a design protocol for RuO2-based core-shell catalysts which exhibit enhanced stability and activity under oxygen evolution reaction (OER) operating conditions. Due to their high activity and favorable stability in acidic electrolytes, Ir and Ru oxides are primary catalysts for the oxygen evolution reaction (OER) in proton-exchange membrane (PEM) electrolyzers. For a future large-scale application, core-shell nanoparticles are an appealing route to minimize the demand for these precious oxides. Here, we employ first-principles density-functional theory (DFT) and ab initio thermodynamics to assess the feasibility of encapsulating a cheap rutile-structured TiO2 core with coherent, monolayer-thin IrO2 or RuO2 films. Resulting from a strong directional dependence of adhesion and strain, a wetting tendency is only obtained for some low-index facets under typical gas-phase synthesis conditions. Thermodynamic stability in particular of lattice-matched RuO2 films is instead indicated for more oxidizing conditions. Intriguingly, the calculations also predict an enhanced activity and stability of such epitaxial RuO2/TiO2 core-shell particles under OER operation.

Machine-learning interatomic potentials, such as Gaussian Approximation Potentials (GAPs), constitute a powerful class of surrogate models to computationally involved first-principles calculations. At a similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training. To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO

Machine-learning interatomic potentials like Gaussian Approximation Potentials (GAPs) constitute a powerful class of surrogate models to computationally involved first-principles calculations. At similar predictive quality but significantly reduced cost, they could leverage otherwise barely tractable extensive sampling as in global surface structure determination (SSD). This efficiency is jeopardized though, if an a priori unknown structural and chemical search space as in SSD requires an excessive number of first-principles data for the GAP training.To this end, we present a general and data-efficient iterative training protocol that blends the creation of new training data with the actual surface exploration process. Demonstrating this protocol with the SSD of low-index facets of rutile IrO2 and RuO2 , the involved simulated annealing on the basis of the refining GAP identifies a number of unknown terminations even in the restricted sub-space of (1×1) surface unit-cells. Especially in an O-poor environment, some of these, then metal-rich terminations, are thermodynamically most stable and are reminiscent of complexions as discussed for complex ceramic materials.

Lithium-graphite intercalation compounds (Li-GICs) are the most popular anode material for modern lithium-ion batteries and have been subject to numerous studies—both experimental and theoretical. However, the system is still far from being consistently understood in detail across the full range of state of charge (SOC). The performance of approaches based on density functional theory (DFT) varies greatly depending on the choice of functional, and their computational cost is far too high for the large supercells necessary to study dilute and non-equilibrium configurations which are of paramount importance for understanding a complete charging cycle. On the other hand, cheap machine learning methods have made some progress in predicting, e.g., formation energetics, but fail to provide the full picture, including electrostatics and migration barriers. Following up on our previous work, we deliver on the promise of providing a complete and affordable simulation framework for Li-GICs. It is based on density functional tight binding (DFTB), which is fitted to dispersion-corrected DFT data using Gaussian process regression (GPR). In this work, we added the previously neglected lithium–lithium repulsion potential and extend the training set to include superdense Li-GICs (LiC6−x, x&gt, 0) and lithium metal, allowing for the investigation of dendrite formation, next-generation modified GIC anodes, and non-equilibrium states during fast charging processes in the future. For an extended range of structural and energetic properties—layer spacing, bond lengths, formation energies and migration barriers—our method compares favorably with experimental results and with state-of-the-art dispersion-corrected DFT at a fraction of the computational cost. We make use of this by investigating some larger-scale system properties—long range Li–Li interactions, dielectric constants and domain-formation—proving our method’s capability to bring to light new insights into the Li-GIC system and bridge the gap between DFT and meso-scale methods such as cluster expansions and kinetic Monte Carlo simulations.

At the cell voltages required to reach technologically viable current densities in proton-exchange membrane (PEM) electrolyzers, IrO2 catalysts are suspected to undergo a transformation to an amorphous hydrous form. Here, we present a systematic ab initio thermodynamics study analyzing the shape and stability of IrO2 nanoparticles in this potential range. Our results confirm a thermodynamic instability of the rutile crystal structure induced by the stabilization of highly oxidized O species at the surface already at onset potentials for the oxygen evolution reaction (OER). Intriguingly, this is preceded by a transformation of the equilibrium shape at even lower potentials. Instead of the well-studied IrO2(110) facets, this shape is dominated by IrO2(111) facets that have hitherto barely received attention. Our findings highlight the need to extend detailed characterization studies to this high-potential range, not the least to establish more suitable active-site models for the OER that may then serve as t...

Antiperovskite glasses such as Li3OCl and doped analogues have been proposed as excellent electrolytes for all-solid-state Li ion batteries (ASSB). Incorporating these electrolytes in ASSBs results in puzzling properties. This Letter describes a theoretical Li3OCl glass created by conventional melt-quench procedures. The ion conductivities are calculated using molecular dynamics based on a polarizable force field that is fitted to an extensive set of density functional theory-based energies, forces, and stresses for a wide range of nonequilibrium structures encompassing crystal, glass, and melt. We find high Li+ ion conductivity in good agreement with experiments. However, we also find that the Cl- ion is mobile as well so that the Li3OCl glass is not a single-ion conductor, with a transference number t + ≈ 0.84. This has important implications for its use as an electrolyte for all-solid-state batteries because the Cl could react irreversibly with the electrodes and/or produce glass decomposition during discharge-charge.

Modeling complex energy materials such as solid-state electrolytes (SSEs) realistically at the atomistic level strains the capabilities of state-of-the-art theoretical approaches. On one hand, the system sizes and simulation time scales required are prohibitive for first-principles methods such as the density functional theory. On the other hand, parameterizations for empirical potentials are often not available, and these potentials may ultimately lack the desired predictive accuracy. Fortunately, modern machine learning (ML) potentials are increasingly able to bridge this gap, promising first-principles accuracy at a much reduced computational cost. However, the local nature of these ML potentials typically means that long-range contributions arising, for example, from electrostatic interactions are neglected. Clearly, such interactions can be large in polar materials such as electrolytes, however. Herein, we investigate the effect that the locality assumption of ML potentials has on lithium mobility and defect formation energies in the SSE Li7P3S11. We find that neglecting long-range electrostatics is unproblematic for the description of lithium transport in the isotropic bulk. In contrast, (field-dependent) defect formation energies are only adequately captured by a hybrid potential combining ML and a physical model of electrostatic interactions. Broader implications for ML-based modeling of energy materials are discussed.

Advanced energy materials 11(26), 2100707 (2021). doi:10.1002/aenm.202100707, Published by Wiley-VCH, Weinheim

Poly(ethylene oxide) (PEO)-based polymers are common hosts in solid polymer electrolytes (SPEs) for high-power energy devices. Molecular simulations have provided valuable molecular insights into structures and ion transport mechanisms of PEO-based SPEs. The calculation of thermodynamic and kinetic properties rely crucially on the dependability of the molecular force fields describing inter- and intra-molecular interactions with the target system. In this work, we reparametrized atomic partial charges for the widely applied optimized potentials for liquid simulations (OPLS) force field of PEO. The revised OPLS force field, OPLSR, improves the calculations of density, thermal expansion coefficient, and the phase transition of the PEO system. In particular, OPLSR greatly enhances the accuracy of the calculated dielectric constant of PEO, which is critical for simulating polymer electrolytes. The reparameterization method was further applied to SPE system of PEO/LiTFSI with O:Li ratio of 16:1. Based on the reparametrized partial charges, we applied separate charge-scaling factors for PEO and Li salts. The charge-rescaled OPLSR model significantly improves the resulting kinetics of Li+ transport while maintaining the accurate description of coordination structures within PEO-based SPE. The proposed OPLSR force field can benefit the future simulation studies of SPE systems.

A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO_{2} facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1×1) terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted (1×1) periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.

Zero strain insertion, high cycling stability, and a stable charge/discharge plateau are promising properties rendering Lithium Titanium Oxide (LTO) a possible candidate for an anode material in solid state Li ion batteries. However, the use of pristine LTO in batteries is rather limited due to its electronically insulating nature. In contrast, reduced LTO shows an electronic conductivity several orders of magnitude higher. Studying bulk reduced LTO, we could show recently that the formation of polaronic states can play a major role in explaining this improved conductivity. In this work, we extend our study toward the lithium-terminated LTO (111) surface. We investigate the formation of polarons by applying Hubbard-corrected density functional theory. Analyzing their relative stabilities reveals that positions with Li ions close by have the highest stability among the different localization patterns.

A Gaussian approximation potential was trained using density-functional theory data to enable a global geometry optimization of low-index rutile IrO_{2} facets through simulated annealing. Ab initio thermodynamics identifies (101) and (111) (1×1) terminations competitive with (110) in reducing environments. Experiments on single crystals find that (101) facets dominate and exhibit the theoretically predicted (1×1) periodicity and x-ray photoelectron spectroscopy core-level shifts. The obtained structures are analogous to the complexions discussed in the context of ceramic battery materials.

Background Computed tomography (CT) of patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) disease depicts the extent of lung involvement in COVID-19 pneumonia. Purpose The aim of the study was to determine the value of quantification of the well-aerated lung obtained at baseline chest CT for determining prognosis in patients with COVID-19 pneumonia. Materials and Methods Patients who underwent chest CT suspected for COVID-19 pneumonia at the emergency department admission between February 17 to March 10, 2020 were retrospectively analyzed. Patients with negative reverse-transcription polymerase chain reaction (RT-PCR) for SARS-CoV-2 in nasal-pharyngeal swabs, negative chest CT, and incomplete clinical data were excluded. CT was analyzed for quantification of well aerated lung visually (%V-WAL) and by open-source software (%S-WAL and absolute volume, VOL-WAL). Clinical parameters included demographics, comorbidities, symptoms and symptom duration, oxygen saturation and laboratory values. Logistic regression was used to evaluate relationship between clinical parameters and CT metrics versus patient outcome (ICU admission/death vs. no ICU admission/ death). The area under the receiver operating characteristic curve (AUC) was calculated to determine model performance. Results The study included 236 patients (females 59/123, 25%; median age, 68 years). A %V-WAL

For symmetric (hermitian) (dense or banded) matrices the computation of eigenvalues and eigenvectors Ax = λBx is an important task, e.g. in electronic structure calculations. If a larger number of eigenvectors are needed, often direct solvers are applied. On parallel architectures the ELPA implementation has proven to be very efficient, also compared to other parallel solvers like EigenExa or MAGMA. The main improvement that allows better parallel efficiency in ELPA is the two-step transformation of dense to band to tridiagonal form. This was the achievement of the ELPA project. The continuation of this project has been targeting at additional improvements like allowing monitoring and autotuning of the ELPA code, optimizing the code for different architectures, developing curtailed algorithms for banded A and B, and applying the improved code to solve typical examples in electronic structure calculations. In this paper we will present the outcome of this project.

Lithium titanium oxide Li4Ti5O12 is an intriguing anode material promising particularly long-life batteries, due to its remarkable phase stability during (dis)charging of the cell. However, its usage is limited by its low intrinsic electronic conductivity. Introducing oxygen vacancies can be one method for overcoming this drawback, possibly by altering the charge carrier transport mechanism. We use Hubbard corrected density functional theory to show that polaronic states in combination with a possible hopping mechanism can play a crucial role in the experimentally observed increase in electronic conductivity. To gauge polaronic charge mobility, we compute the relative stabilities of different localization patterns and estimate polaron hopping barrier heights.

Accurate and transferable models of reaction kinetics are of key importance for chemical reactors on both laboratory and industrial scale. Usually, setting up such models requires a detailed mechanistic understanding of the reaction process and its interplay with the reactor setup. We present a data driven approach which analyzes the influence of process parameters on the reaction rate to identify locally approximated effective rate laws without prior knowledge and assumptions. The algorithm we propose determines relevant model terms from a polynomial ansatz employing well established statistical methods. For the optimization of the model parameters special emphasize is put on the robustness of the results by taking not only the quality of the fit but also the distribution of errors into account in a multi-objective optimization. We demonstrate the flexibility of this approach based on artificial kinetic data sets from microkinetic models. This way, we show that the kinetics of both the classical HBr reaction and a prototypical catalytic cycle are automatically reproduced. Further, combining our approach with experimental screening designs we illustrate how to efficiently explore kinetic regimes by using the example of the catalytic oxidation of CO.

We present a novel approach for calculating the static dielectric permittivity profile of a liquid–liquid interface (LLI) from molecular dynamics simulations. To obtain well-defined features, comparable to those observed at solid–liquid interfaces, we find it essential to reference to the instantaneous liquid–liquid interface rather than the more commonly used average Gibbs interface. We provide a coarse-grained approach for the practical definition of the instantaneous interface and present numerical results for the prototypical water/1,2-dichloroethane system. These results show that the parallel components of the dielectric permittivity tensor can be accurately extracted. In contrast, the perpendicular component does not converge to the correct bulk value at large distances from the LLI, highlighting a flaw in the regularly applied coarse-graining procedure.

Rapid deactivation presently limits a wide spread use of high-temperature solid oxide cells (SOCs) as otherwise highly efficient chemical energy converters. With deactivation triggered by the ongoing conversion reactions, an atomic-scale understanding of the active triple-phase boundary between electrolyte, electrode, and gas phase is essential to increase cell performance. Here, a multi-method approach is used comprising transmission electron microscopy and first-principles calculations and molecular simulations to untangle the atomic arrangement of the prototypical SOC interface between a lanthanum strontium manganite (LSM) anode and a yttria-stabilized zirconia (YSZ) electrolyte in the as-prepared state after sintering. An interlayer of self-limited width with partial amorphization and strong compositional gradient is identified, thus exhibiting the characteristics of a complexion that is stabilized by the confinement between two bulk phases. This offers a new perspective to understand the function of SOCs at the atomic scale. Moreover, it opens up a hitherto unrealized design space to tune the conversion efficiency.

With the advent of efficient electronic structure methods, effective continuum solvation methods have emerged as a way to, at least partially, include solvent effects into simulations without the need for expensive sampling over solvent degrees of freedom. The multipole moment expansion (MPE) model, while based on ideas initially put forward almost 100 years ago, has recently been updated for the needs of modern electronic structure calculations. Indeed, for an all-electron code relying on localized basis sets and-more importantly-a multipole moment expansion of the electrostatic potential, the MPE method presents a particularly cheap way of solving the macroscopic Poisson equation to determine the electrostatic response of a medium surrounding a solute. In addition to our implementation of the MPE model in the FHI-aims electronic structure theory code [ Blum , V. ; Comput. Phys. Commun. 2009 , 180 , 2175 - 2196 , DOI: 10.1016/j.cpc.2009.06.022 ], we describe novel algorithms for determining equidistributed points on the solvation cavity-defined as a charge density isosurface-and the determination of cavity surface and volume from just this collection of points and their local density gradients. We demonstrate the efficacy of our model on an analytically solvable test case, against high-accuracy finite-element calculations for a set of ≈140000 2D test cases, and finally against experimental solvation free energies of a number of neutral and singly charged molecular test sets [ Andreussi , O. ; J. Chem. Phys. 2012 , 136 , 064102 , DOI: 10.1063/1.3676407 ; Marenich , A. V. ; Minnesota Solvation Database , Version 2012; University of Minnesota : Minneapolis, MN, USA , 2012 . ]. In all test cases we find that our MPE approach compares very well with given references at computational overheads20% and sometimes much smaller compared to a plain self-consistency cycle.

Aberrant pH is characteristic of many pathologies such as ischemia, inflammation or cancer. Therefore, a non-invasive and spatially resolved pH determination is valuable for disease diagnosis, characterization of response to treatment and the design of pH-sensitive drug-delivery systems. We recently introduced hyperpolarized [1,5-13C2]zymonic acid (ZA) as a novel MRI probe of extracellular pH utilizing dissolution dynamic polarization (DNP) for a more than 10000-fold signal enhancement of the MRI signal. Here we present a strategy to enhance the sensitivity of this approach by deuteration of ZA yielding [1,5-13C2, 3,6,6,6-D4]zymonic acid (ZAd), which prolongs the liquid state spin lattice relaxation time (T1) by up to 39 % in vitro. Measurements with ZA and ZAd on subcutaneous MAT B III adenocarcinoma in rats show that deuteration increases the signal-to-noise ratio (SNR) by up to 46 % in vivo. Furthermore, we demonstrate a proof of concept for real-time imaging of dynamic pH changes in vitro using ZAd, potentially allowing for the characterization of rapid acidification/basification processes in vivo.

Lithium–titanium-oxide (Li4Ti5O12, LTO) is unique among battery materials due to its exceptional cyclability and high rate capability. This performance is believed to derive at least partly from the occupational disorder introduced via mixed Li/Ti occupancy in the LTO spinel-like structure. We explore the vast configuration space accessible during high-temperature LTO synthesis by Monte Carlo sampling and indeed find lowest-energy structures to be characterized by a high degree of microscopic inhomogeneity. Dynamical simulations in corresponding configurations reveal the dominant fraction of Li ions to be immobile on nanosecond time scales. However, Ti antisite-like defects stabilized by the configurational disorder give rise to a novel correlated ion diffusion mechanism. The resulting fast but localized diffusion could be a key element in the sudden rise in conductivity found in LTO in the early stages of charging and questions the validity of ion mobility measurements for this and other configurationall...

The solution of (generalized) eigenvalue problems for symmetric or Hermitian matrices is a common subtask of many numerical calculations in electronic structure theory or materials science. Depending on the scientific problem, solving the eigenvalue problem can easily amount to a sizeable fraction of the whole numerical calculation, and quite often is even the dominant part by far. For researchers in the field of computational materials science, an efficient and scalable solution of the eigenvalue problem is thus of major importance. The ELPA-library (Eigenvalue SoLvers for Petaflop-Applications) is a well-established dense direct eigenvalue solver library, which has proven to be very efficient and scalable up to very large core counts. It is in a wide-spread production use on a large variety of HPC systems worldwide, and is applied by many codes in the field of materials science. In this paper, we describe the latest optimizations of the ELPA-library for new HPC architectures of the Intel Skylake processor family with an AVX-512 SIMD instruction set, or for HPC systems accelerated with recent GPUs. Apart from those direct hardware-related optimizations, we also describe a complete redesign of the API in a modern modular way, which, apart from a much simpler and more flexible usability, leads to a new path to access system-specific performance optimizations. In order to ensure optimal performance for a particular scientific setting or a specific HPC system, the new API allows the user to influence in a straightforward way the internal details of the algorithms and of performance-critical parameters used in the ELPA-library. On top of that, we introduce an autotuning functionality, which allows for finding the best settings in a self-contained automated way, without the need of much user effort. In situations where many eigenvalue problems with similar settings have to be solved consecutively, the autotuning process of the ELPA-library can be done “on-the-fly”, without the need of preceding the simulation with an “artificial” autotuning step. Practical applications from materials science which rely on reaching a numerical convergence limit by so-called self-consistency iterations can profit from the on-the-fly autotuning. On some examples of scientific interest, simulated with the FHI-aims application, the advantages of the latest optimizations of the ELPA-library are demonstrated.

Antiperovskite glasses such as Li

We use the gauge-including projector augmented-waves (GIPAW) method to report, for the first time, theoretical 27Al Knight shifts in metallic systems other than metallic Al. We consider metallic Al and a set of six intermetallic compounds, for which experimental chemical shifts were recently made available in the literature. The orbital and spin components of the chemical shielding tensors are computed from the same ground-state spin-polarized electronic structure, converged under the influence of a uniform external magnetic field. A linear response formalism is used to compute the orbital part, while the spin part is approximated by the linear relationship between the external field and the Fermi contact contribution to the induced magnetic hyperfine field at the nuclear position. Core spin-polarization effects are taken into account by means of a perturbative approach. Our results show that the GIPAW approach yields reasonably acceptable chemical shifts with affordable k-meshes in the irreducible Brillo...

Lithium ion batteries have been a central part of consumer electronics for decades. More recently, they have also become critical components in the quickly arising technological fields of electric mobility and intermittent renewable energy storage. However, many fundamental principles and mechanisms are not yet understood to a sufficient extent to fully realize the potential of the incorporated materials. The vast majority of concurrent lithium ion batteries make use of graphite anodes. Their working principle is based on intercalation–the embedding and ordering of (lithium-) ions in the two-dimensional spaces between the graphene sheets. This important process–it yields the upper bound to a battery's charging speed and plays a decisive role for its longevity–is characterized by multiple phase transitions, ordered and disordered domains, as well as non-equilibrium phenomena, and therefore quite complex. Such complexity emerges particularly at low states of charge (SOC), and complicates both the interpretation of experiments and the computational modelling. From a computational standpoint, targeted system sizes compatible with the SOC range of interest are inaccessible to first-principles calculations, yet require first-principles treatment of key effects such as dispersion and long-range electrostatics. Density Functional Tight Binding (DFTB), a semi-empirical approximation to DFT, offers a high-quality trade-off between accuracy and speed. However, this advantage comes at the cost–or rather initial investment–of pairwise parametrization. As no Li-C DFTB parameters were publicly available yet, we produced a parameter set specifically tailored to this system, employing a parametrization strategy [1] that combines global optimization of electronic parameters via Particle Swarm Optimization (PSO) [2] with our recently developed approach that uses Gaussian Process Regression (GPR) [3] to machine-learn the repulsive potential. Using the resulting parametrization, we are able to reproduce experimental reference structures at a level of accuracy which is in no way inferior to much more costly ab initio methods. We present structural properties and diffusion barriers for some exemplary system states. Additionally, we are able for the first time to resolve the full Potential Energy Surface (PES) of Li motion in stage-I and stage-II LiC108 (SOC 5%). The PES contains information that enables us to implement, and in perspective discuss, both kinetic Monte Carlo (kMC) models of Li-ion mobility in the graphite host, and free-energy sampling which ultimately yields the computed voltage profile of the anode. [1] Panosetti et al., https://arxiv.org/abs/1904.13351 [2] Chou et al., J. Chem. Theory Comput. 2016, 12, 1, 53–64 [3] Panosetti et al., J. Chem. Theory Comput. 2020, 16, 4, 2181–2191 Figure 1

Zero strain insertion, high cycling stability, and a stable charge/discharge plateau are promising properties rendering Lithium Titanium Oxide (LTO) a possible candidate for an anode material in solid state Li ion batteries. However, the use of pristine LTO in batteries is rather limited due to its electronically insulating nature. In contrast, reduced LTO shows an electronic conductivity several orders of magnitude higher. Studying bulk reduced LTO, we could show recently that the formation of polaronic states can play a major role in explaining this improved conductivity. In this work, we extend our study toward the lithium-terminated LTO (111) surface. We investigate the formation of polarons by applying Hubbard-corrected density functional theory. Analyzing their relative stabilities reveals that positions with Li ions close by have the highest stability among the different localization patterns.

Controllable synthesis of defect-free graphene is crucial for applications since the properties of graphene are highly sensitive to any deviations from the crystalline lattice. We focus here on the emerging use of liquid Cu catalysts, which has high potential for fast and efficient industrial-scale production of high-quality graphene. The interface between graphene and liquid Cu is studied using force field and ab initio molecular dynamics, revealing a complete or partial embedding of finite-sized flakes. By analyzing flakes of different sizes we find that the size-dependence of the embedding can be rationalized based on the energy cost of embedding versus bending the graphene flake. The embedding itself is driven by the formation of covalent bonds between the under-coordinated edge C atoms and the liquid Cu surface, which is accompanied by a significant charge transfer. In contrast, the central flake atoms are located around or slightly above 3 {\AA} from the liquid Cu surface and exhibit weak vdW-bonding and much lower charge transfer. The structural and electronic properties of the embedded state revealed in our work provides the atomic-scale information needed to develop effective models to explain the special growth observed in experiments where various interesting phenomena such as flake self-assembly and rotational alignment, high growth speeds and low defect densities in the final graphene product have been observed., Comment: This article may be downloaded for personal use only. Any other use requires prior permission of the author and AIP Publishing. This article appeared in J. Chem. Phys. 153, 074702 (2020) and may be found at https://doi.org/10.1063/5.0020126