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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. 

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.