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Register a new userCharting Lattice Thermal Conductivity of Inorganic Crystals
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Thermal conductivity is a fundamental material property but challenging to predict, with less than 5% out of about $10^5$ synthesized inorganic materials being documented. In this work, we extract the structural chemistry that governs lattice thermal conductivity, by combining graph neural networks and random forest approaches. We show that both mean and variation of unit-cell configurational properties, such as atomic volume and bond length, are the most important features, followed by mass and elemental electronegativity. We chart the structural chemistry of lattice thermal conductivity into extended van-Arkel triangles, and predict the thermal conductivity of all known inorganic materials in the Inorganic Crystal Structure Database. For the latter, we develop a transfer learning framework extendable for other applications.
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The transparent semiconductor In$_{2}$O$_{3}$ is a technologically important material. It combines optical transparency in the visible frequency range and sizeable electric conductivity. We present a study of thermal conductivity of In$_{2}$O$_{3}$ crystals and find that around 20 K, it peaks to a value as high as 5,000 WK$^{-1}$m$^{-1}$, comparable to the peak thermal conductivity in silicon and exceeded only by a handful of insulators. The amplitude of the peak drastically decreases in presence of a type of disorder, which does not simply correlate with the density of mobile electrons. Annealing enhances the ceiling of the phonon mean free path. Samples with the highest thermal conductivity are those annealed in the presence of hydrogen. Above 100 K, thermal conductivity becomes sample independent. In this intrinsic regime, dominated by phonon-phonon scattering, the magnitude of thermal diffusivity, $D$ becomes comparable to many other oxides, and its temperature dependence evolves towards $T^{-1}$. The ratio of $D$ to the square of sound velocity yields a scattering time which obeys the expected scaling with the Planckian time.
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We unify two prevailing theories of thermal quenching (TQ) in rare-earth-activated inorganic phosphors - the cross-over and auto-ionization mechanisms - into a single predictive model. Crucially, we have developed computable descriptors for activator environment stability from ab initio molecular dynamics simulations to predict TQ under the cross-over mechanism, which can be augmented by a band gap calculation to account for auto-ionization. The resulting TQ model predicts the experimental TQ in 29 known phosphors to within ~ 3-8%. Finally, we have developed an efficient topological approach to rapidly screen vast chemical spaces for the discovery of novel, thermally robust phosphors.
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