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  1. Thermal radiation emission poses a challenge for using most existing ceramics for thermal environmental barrier coatings of gas-turbine engines operating at temperatures approaching 1500 °C and beyond. This study presents a strategy for photon transport mitigation in fully dense ceramic composites by increasing the refractive index mismatch between the matrix and particle oxides. We investigate this strategy by analyzing radiative properties in 118 different rare-earth pyrosilicate–pyrochlore ceramic composites. We use density functional theory to predict the optical properties of homogeneous oxides and Lorentz–Mie theory to model scattering at the interfaces of the composite. Our findings demonstrate that increasing the refractive mismatch between the matrix and oxide phases can significantly reduce radiative heat flux. Furthermore, we show that additional thermal radiation suppression can be achieved by increasing the particle size. Our theoretical investigation has the potential to aid in the discovery of new coating ceramic composites and guide their microstructural design. 
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    Free, publicly-accessible full text available February 1, 2025
  2. Saddle point search schemes are widely used to identify the transition state of different processes, like chemical reactions, surface and bulk diffusion, surface adsorption, and many more. In solid-state materials with relatively large numbers of atoms, the minimum mode following schemes such as dimer are commonly used because they alleviate the calculation of the Hessian on the high-dimensional potential energy surface. Here, we show that the dimer search can be further accelerated by leveraging Gaussian process regression (GPR). The GPR serves as a surrogate model to feed the dimer with the required energy and force input. We test the GPR-accelerated dimer method for predicting the diffusion coefficient of vacancy-mediated self-diffusion in body-centered cubic molybdenum and sulfur diffusion in hexagonal molybdenum disulfide. We use a multitask learning approach that utilizes a shared covariance function between energy and force input, and we show that the multitask learning significantly improves the performance of the GPR surrogate model compared to previously used learning approaches. Additionally, we demonstrate that a translation-hop sampling approach is necessary to avoid overfitting the GPR surrogate model to the minimum-mode-following pathway and thus succeeding in locating the saddle point. We show that our method reduces the number of evaluations compared to a conventional dimer method. 
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    Free, publicly-accessible full text available January 24, 2025
  3. Abstract Predicting the synthesizability of hypothetical crystals is challenging because of the wide range of parameters that govern materials synthesis. Yet, exploring the exponentially large space of novel crystals for any future application demands an accurate predictive capability for synthesis likelihood to avoid a haphazard trial-and-error. Typically, benchmarks of synthesizability are defined based on the energy of crystal structures. Here, we take an alternative approach to select features of synthesizability from the latent information embedded in crystalline materials. We represent the atomic structure of crystalline materials by three-dimensional pixel-wise images that are color-coded by their chemical attributes. The image representation of crystals enables the use of a convolutional encoder to learn the features of synthesizability hidden in structural and chemical arrangements of crystalline materials. Based on the presented model, we can accurately classify materials into synthesizable crystals versus crystal anomalies across a broad range of crystal structure types and chemical compositions. We illustrate the usefulness of the model by predicting the synthesizability of hypothetical crystals for battery electrode and thermoelectric applications. 
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