The accurate prediction of microstructures under mechanical stresses is crucial for understanding materials behavior, especially in body-centered cubic (bcc) metals where the deformation phenomena remain elusive. In this work, we introduce a continuum framework, linked to crystallographic considerations and atomistic simulations, to model deformation twinning in bcc systems using quantum annealing. This requires formulation of the problem in terms of the global minimization of an Ising Hamiltonian, with coefficients reflecting the elastic interactions between discretization domains. We perform qualitative comparisons with atomistic simulations and quantitative benchmarking against analytical solutions in well-defined setups, and demonstrate the scalability to larger, polycrystalline specimens, where atomistic methods become prohibitive due to high computational costs, outputting qualitatively similar pictures as experiments. The scale-bridging, quantum annealing based model provides an efficient and novel computational framework for determining equilibrium microstructures in single crystals and polycrystals under mechanical stresses, with emphasis on systems where twinning is a dominant deformation mechanism.

Electrocatalysts support crucial industrial processes and emerging decarbonization technologies, but their design is hindered by structural and compositional changes during operation, especially at application-relevant current densities. Here we use operando X-ray spectroscopy and modelling to track, and eventually direct, the reconstruction of iron sulfides and oxides for the oxygen evolution reaction. We show that inappropriate activation protocols lead to uncontrollable Fe oxidation and irreversible catalyst degradation, compromising stability and reliability and precluding predictive design. Based on these, we develop activation programming strategies that, considering the thermodynamics and kinetics of surface reconstruction, offer control over precatalyst oxidation. This enables reliable predictions and the design of active and stable electrocatalysts. In a NixFe1-xS2 model system, this leads to a threefold improvement in durability after programmed activation, with a cell degradation rate of 0.12 mV h-1 over 550 h (standard operation: 0.29 mV h-1, constrained to 200 h), in an anion exchange membrane water electrolyser operating at 1 A cm-2. This work bridges predictive modelling and experimental design, improving the electrocatalyst reliability for industrial water electrolysis and beyond at high current densities.

We explore the mechanical properties of lithium metal and investigate how specimen size, crystallographic orientation, temperature and strain rate affect its deformation behavior. In single crystals, it is found that anisotropy plays an essential role both for elasticity and plasticity. We demonstrate that Li single crystal nanopillars plastically deform either by dislocation slip or by twinning, depending on orientation, loading mode (tension versus compression) and strain rate. In the cases of slip, deformation is predominantly mediated by dislocations with a Burgers vector of 1/2 < 111 >, which are active on different planes. Conversely, twinning occurs singularly on {112} planes and displays a twinning/anti-twinning anisotropy, resulting in the strong dependence on orientation and loading mode observed. In terms of size dependence, specimens undergoing dislocation slip show a pronounced 'smaller is stronger' trend in their yield strength. However, for specimens deforming exclusively by twinning, size effects are notably less significant. In polycrystalline nanostructures, opposed to the single crystal findings, the yield strength decreases with finer grain size in the spirit of an inverse Hall-Petch relationship, with grain boundaries being the central effect influencing their mechanical behavior.

We revisit the thermodynamics of the Li–Na system below the eutectic temperature (≈350K), where both elements tend to form essentially immiscible bcc phases. Ab initio calculations are used to extract the energetics and to optimize the non-ideal interaction terms of the Gibbs energy, especially in the dilute limit. The obtained optimized description is used to predict the low-temperature (below 350K) metallic phase diagram and the spinodal regime. Furthermore, elastic coherency effects are included. The resulting descriptions are used to interpret and predict the microstructure formation in Li–Na metal anodes for all-solid state batteries, providing a theoretical composition range where such microstructure is expected to form.

Lithium metal anodes (LMA) increase the energy density of lithium-ion batteries, but the formation of lithium dendrites above a critical charging current (CCD) is still a severe safety issue that limits their wide industrial application. In this work, we present a simple, scalable method to improve the properties of LMA and increase the CCD by physical mixing with a small amount of Na metal, leading to a formation of self-organized 3D interfacial structures during cycling. The physical premixing of Li and Na metal results in excellent dispersion of the metals without phase separation or clustering. To demonstrate the effectiveness of these LiNa anodes in solidstate cells with oxide-ceramic Li6.45Al0.05La3Zr1.6Ta0.4O12 (LLZO) separators, we melt-quench them directly onto the LLZO surface. The application of a special formation protocol during cycling leads to the in-situ formation of a 3D Na-metal interfacial structure, which improves the cell performance. The symmetric cells prepared in this way were operated without external pressure (0.1 MPa) and showed record CCDs for planar interfaces of over 5.0 mA center dot cm- 2, cycling stability of over 1200 cycles, and a total stripping capability of up to 100 mu m Li metal, corresponding to a capacity of 21 mAh center dot cm- 2. Most remarkably, our approach resulted in a very low impedance of the Li/LLZO interface, which remained constant even at high stripping/plating rates. The new approach provides an industrially scalable method for fabricating next generation LMAs with an inherently reduced tendency to dendrite formation, which can be readily utilized in a variety of next-generation lithium batteries.

We present a detailed derivation of the elastic energy of a homogeneous, isotropic linear elastic medium consisting of different coherent martensite variants or phases and its mapping to an Ising model, as required for an efficient quantum annealing determination of the equilibrium microstructure. The approach is demonstrated for a sample with a large number of grains with a tetragonal eigenstrain. Furthermore, we illustrate how the elastic effects may lead to the formation of ion conducting channels in the doped solid electrolyte Li 7 La 3 Zr 2 O 12 (LLZO). Apart from bulk elastic and chemical effect we demonstrate how to include interfacial effects into the quantum annealing approach and emphasize the importance of high precision elastic calculations.

We demonstrate the use of quantum annealing for the selection of multiple martensite variants in a microstructure with long-range coherency stresses and external mechanical load. The general approach is illustrated for martensites with four different variants based on the minimization of the linear elastic energy. The equilibrium variant distribution is then analyzed under application of tensile and shear strains and for different values of the considered shear and tetragonal contributions of the different martensite variants. The interface orientations between different domains of variants can be explained using the perspective of the elastic energy anisotropy for regular stripe patterns. For random grain orientations, the response to an external elastic strain is weaker and variant changes can be interpreted based on the rotated eigenstrain tensor.

Many potential use cases for machine learning in chemistry and materials science suffer from small dataset sizes, which demands special care for the model design in order to deliver reliable predictions. Hence, feature selection as the key determinant for dataset design is essential here. We propose a practical and efficient feature filter strategy to determine the best input feature candidates. We illustrate this strategy for the prediction of adsorption energies based on a public dataset and sublimation enthalpies using an in-house training dataset. The input of adsorption energies reduces the feature space from 12 dimensions to two and still delivers accurate results. For the sublimation enthalpies, three input configurations are filtered from 14 possible configurations with different dimensions for further productive predictions as being most relevant by using our feature filter strategy. The best extreme gradient boosting regression model possesses a good performance and is evaluated from statistical and theoretical perspectives, reaching a level of accuracy comparable to density functional theory computations and allowing for physical interpretations of the predictions. Overall, the results indicate that the feature filter strategy can help interdisciplinary scientists without rich professional AI knowledge and limited computational resources to establish a reliable small training dataset first, which may make the final machine learning model training easier and more accurate, avoiding time-consuming hyperparameter explorations and improper feature selection.

We propose a data-driven strategy for parameter selection in phase field nucleation models using machine learning and apply it to oxide nucleation in Fe-Cr alloys. A grand potential-based phase field model, incorporating Langevin noise, is employed to simulate oxide nucleation and benchmarked against the Johnson-Mehl-Avrami-Kolmogorov model. Three independent parameters in the phase field simulations (Langevin noise strength, numerical grid discretization and critical nucleation radius) are identified as essential for accurately modeling the nucleation behavior. These parameters serve as input features for machine learning classification and regression models. The classification model categorizes nucleation behavior into three nucleation density regimes, preventing invalid nucleation attempts in simulations, while the regression model estimates the appropriate Langevin noise strength, significantly reducing the need for time-consuming trial-and-error simulations. This data-driven approach improves the efficiency of parameter selection in phase field models and provides a generalizable method for simulating nucleation-driven microstructural evolution processes in various materials.

Quantum annealing is an efficient technology to determine ground state configurations of discrete binary optimization problems, described through Ising Hamiltonians. Here we show that-at very low computational cost-finite temperature properties can be calculated. The approach is most efficient at low temperatures, where conventional approaches like Metropolis Monte Carlo sampling suffer from high rejection rates and therefore large statistical noise. To demonstrate the general approach, we apply it to spin glasses and Ising chains.

The Crofer 22 APU alloy is a frequently used metallic material to manufacture interconnects in solid oxide fuel cells. However, the formation and evaporation of Cr2O3 not only increases the electrical resistance but also leads to the Cr-related degradation over the service time. In order to investigate the growth kinetics of Cr-oxide, i.e., Cr2O3, the multi-phase field model coupled with reliable CALPHAD databases is employed. The phase field simulation results are benchmarked with the predictions of Wagner's theory. Moreover, we evidence the influence of the temperature and Cr concentration on the ferritic matrix phase and the oxygen concentration at the Cr2O3/gas interface on the growth kinetics of Cr-oxide, paving the way for further investigations of Cr-related solid oxide fuel cell degradation processes.

The influence of different processing routes and grain size distributions on the character of the grain boundaries in Li7La3Zr2O12 (LLZO) and the potential influence on failure through formation of percolating lithium metal networks in the solid electrolyte are investigated. Therefore, high quality hot-pressed Li7La3Zr2O12 pellets are synthesised with two different grain size distributions. Based on the electron backscatter diffraction measurements, the grain boundary network including the grain boundary distribution and its connectivity via triple junctions are analysed concerning potential Li plating along certain susceptible grain boundary clusters in the hot-pressed LLZO pellets. Additionally, the study investigates the possibility to interpret short-circuiting caused by Li metal plating or penetration in all-solid-state batteries through percolation mechanisms in the solid electrolyte microstructure, in analogy to grain boundary failure processes in metallic systems.

We demonstrate the use and benefits of quantum annealing approaches for the determination of equilibrated microstructures in shape memory alloys and other materials with long-range elastic interaction between coherent grains and their different martensite variants and phases. After a one dimensional illustration of the general approach, which requires to formulate the energy of the system in terms of an Ising Hamiltonian, we use distant dependent elastic interactions between grains to predict the variant selection for different transformation eigenstrains. The results and performance of the computations are compared to classical algorithms, demonstrating that the new approach can lead to a significant acceleration of the simulations. Beyond a discretization using simple cuboidal elements, also a direct representation of arbitrary microstructures is possible, allowing fast simulations with currently up to several thousand grains.

Due to the high operating temperature of solid oxide fuel cells, various chemical reactions can lead to degradation, for instance, based on Cr 2 O 3 gas phase evaporation from the interconnect and subsequent reaction with SrO which originates from LSCF cathodes. Therefore, investigations of the potential chemical reactions and the sublimation process play an extremely important role to study the related chemical reactions in SOFCs. Here we propose a practical physical model based on density functional theory calculations and statistical mechanics to predict the vapor pressure of pure substances. This model is allowed to extend the thermodynamic database and to analyze the resulting chemical reactions inducing the new phase formation which leads to the degradation of SOFCs. These investigations serve as a theoretical basis to understand and reduce degradation phenomena from a thermodynamic and kinetic perspective.

We develop a theoretical model to predict the sublimation vapor pressure of pure substances. Moreover, we present a simple monoatomic molecule approximation, which reduces the complexity of the vapor pressure expression for polyatomic gaseous molecules at a convincing level of accuracy, with deviations of the Arrhenius prefactor for NaCl and NaF being 5.02% and 7.08%, respectively. The physical model is based on ab initio calculations, statistical mechanics, and thermodynamics. We illustrate the approach for Ni, Cr, Cu (metallic bond), NaCl, NaF, ZrO2 (ionic bond) and SiO2 (covalent bond). The results are compared against thermodynamic databases, which show high accuracy of our theoretical predictions, and the deviations of the predicted sublimation enthalpy are typically below 10%, for Cu even only 0.1%. Furthermore, the partial pressures caused by gas phase reactions are also explored, showing good agreement with experimental results.

The influence of co-substitutions on the structural and mechanical properties of garnet structured Li7La3Zr2O12 (LLZO) is investigated. Ab initio simulations of the cubic phase under Al and Ta substitutions are performed for an analysis of substitution dependencies on lattice constants, elastic moduli and hardness. The use of the differential effective medium theory methods enables a scale bridging description towards porous LLZO, with a 27% decay of Young's modulus for a porosity of 10%, compared to dense LLZO.

The prediction of the degradation of lithium-ion batteries is essential for various applications and optimized recycling schemes. In order to address this issue, this study aims to predict the cycle lives of lithium-ion batteries using only data from early cycles. To reach such an objective, experimental raw data for 121 commercial lithium iron phosphate/graphite cells are gathered from the literature. The data are analyzed, and suitable input features are generated for the use of different machine learning algorithms. A final accuracy of 99.81% for the cycle life is obtained with an extremely randomized trees model. This work shows that data-driven models are able to successfully predict the lifetimes of batteries using only early-cycle data. That aside, a considerable reduction in errors is seen by incorporating data management and physical and chemical understanding into the analysis.

Lower oxygen vacancy formation energy is one of the requirements for air electrode materials in solid oxide cells applications. We introduce a transfer learning approach for oxygen vacancy formation energy prediction for some ABO3 perovskites from a two-species-doped system to four-species-doped system. For that, an artificial neural network is used. Considering a two-species-doping training data set, predictive models are trained for the determination of the oxygen vacancy formation energy. To predict the oxygen vacancy formation energy of four-species-doped perovskites, a formally similar feature space is defined. The transferability of predictive models between physically similar but distinct data sets, i.e., training and testing data sets, is validated by further statistical analysis on residual distributions. The proposed approach is a valuable supporting tool for the search for novel energy materials.

We develop a three-phase field model for the simulation of eutectic and eutectoid transformations on the basis of a nondiagonal model obeying Onsager relations for a kinetic cross coupling between diffusion and the phase fields. This model overcomes the limitations of existing phase field models concerning the fulfillment of local equilibrium boundary conditions at the transformation fronts in the case of a finite diffusional contrast between the phases. We benchmark our model in the well understood one-sided case with diffusion only in the parent phase against results from the literature. In addition to this solidification scenario, the case of solid-state transformations with diffusion in the growing phases is investigated. Our simulations validate the relevance of the theory developed by Ankit et al. [Acta Mater. 61, 4245 (2013)], that describes in a single frame the two limiting regimes where diffusion mainly takes place whether in the mother phase or in the growing phases. In both the one-sided and two-sided cases, we verify the necessity of the kinetic cross coupling for quantitative phase field simulations.

We revisit recent findings on experimental and modeling investigations of bainitic transformations under the influence of external stresses and pre-strain during the press hardening process. Experimentally, the transformation kinetics in 22MnB5 under various tensile stresses are studied both on the macroscopic and microstructural level. In the bainitic microstructure, the variant selection effect is analyzed with an optimized prior-austenite grain reconstruction technique. The resulting observations are expressed phenomenologically using a autocatalytic transformation model, which serves for further scale bridging descriptions of the underlying thermo-chemo-mechanical coupling processes during the bainitic transformation. Using analyses of orientation relationships, thermodynamically consistent and nondiagonal phase field models are developed, which are supported by ab initio generated mechanical parameters. Applications are related to the microstructure evolution on the sheaf, subunit, precipitate and grain boundary level.