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Abstract Entropic stabilization has evolved into a strategy to create new oxide materials and realize novel functional properties engineered through the alloy composition. Achieving an atomistic understanding of these properties to enable their design, however, has been challenging due to the local compositional and structural disorder that underlies their fundamental structure-property relationships. Here, we combine high-throughput atomistic calculations and linear regression algorithms to investigate the role of local configurational and structural disorder on the thermodynamics of vacancy formation in (MgCoNiCuZn)O-based entropy-stabilized oxides (ESOs) and their influence on the electrical properties. We find that the cation-vacancy formation energies decrease with increasing local tensile strain caused by the deviation of the bond lengths in ESOs from the equilibrium bond length in the binary oxides. The oxygen-vacancy formation strongly depends on structural distortions associated with the local configuration of chemical species. Vacancies in ESOs exhibit deep thermodynamic transition levels that inhibit electrical conduction. By applying the charge-neutrality condition, we determine that the equilibrium concentrations of both oxygen and cation vacancies increase with increasing Cu mole fraction. Our results demonstrate that tuning the local chemistry and associated structural distortions by varying alloy composition acts an engineering principle that enables controlled defect formation in multi-component alloys. 

This paper presents a new approach for predicting thermodynamic properties of perovskites that harnesses deep learning and crystal structure fingerprinting based on Hirshfeld surface analysis. It is demonstrated that convolutional neural network methods capture critical features embedded in two-dimensional Hirshfeld surface fingerprints that enable a quantitative assessment of the formation energy of perovskites. Building on our recent work on lattice parameter prediction from Hirshfeld surface calculations, we show how transfer learning can be used to speed up the training of the neural network, allowing multiple properties to be trained using the same feature extraction layers. We also predict formation energies for various perovskite polymorphs, and our predictions are found to give generally improved performance over a well-established graph network method, but with the methods better suited to different types of datasets. Analysis of the structure types within the dataset reveals the Hirshfeld surface-based method to excel for the less symmetric and similar structures, while the graph network performs better for very symmetric and similar structures. 

This Letter describes the use of deep learning methods on Hirshfeld surface representations of crystal structure, as an automated means of predicting lattice parameters in cubic inorganic perovskites. While Hirshfeld Surface Analysis is a well-established tool in organic crystallography, we also introduce modified computational protocols for Hirshfeld Surface Analysis tailored specifically to account for nuanced but important differences dealing with inorganic crystals. We demonstrate how two-dimensional Hirshfeld surface fingerprints can serve as a rich “database” of information encoding the complexity of relationships between chemical bonding and bond geometry characteristics of perovskites. Our results are compared with other studies on lattice parameter prediction involving both experimental and computationally derived data, and it is shown that our approach is an improvement over other reported methods. The paper concludes by discussing how this work opens new avenues for data-driven high throughput computational predictions of structure–property relationships involving complex crystal chemistries. 

Germanium (Ge) is deemed as one of the most promising alloying anodes for rechargeable lithium‐ion batteries (LIBs) due to its large theoretical capacity, high electrical conductivity, fast lithium‐ion diffusivity, and mechanical robustness. However, Ge‐based anodes suffer from large volume changes during lithiation and delithiation, which can deteriorate their electrochemical performance rapidly. Herein, the large volume change issue is effectively addressed using an asymmetric membrane structure that is prepared using a phase‐inversion method in combination with hydrogen peroxide etching and surface coating. The asymmetric Ge membrane etched by ≈30 wt% H₂O₂at 90 °C for 30 s demonstrates a capacity retention higher than 80% in 50 cycles at 160 mA g⁻¹. Coating the H₂O₂‐etched Ge membrane with carbonaceous membranes can further improve the retention up to 95% in 50 cycles at 160 mA g⁻¹, whereas ≈100% capacity of 700 mAh g⁻¹can be maintained in 170 cycles at 400 mA g⁻¹. A combination of electron microscopy, spectrophotometry, and X‐ray analyses confirms the electrochemical performance of asymmetric Ge membranes as the LIB anode can be significantly affected by membrane geometry, the duration of hydrogen peroxide etching, carbonaceous membrane coating, and Ge concentration.