Figure 5. Electronic structure of (top) 3D MAPbI 3 , i.e., n = ∞, (middle) 2D (BA) 2 (MA)Pb 2 I 7 , and (bottom) 2D (BA) 2 PbI 4 .

The thermal properties of yttrium-stabilized lithium zirconate phosphate [LZP: Li1+x+yYxZr2−x(PO4)3 with x = 0.15, −0.2 ≤ y ≤ 0.4 and with x = 0.0, y = 0.0] are presented over a wide temperature range from 30 to 973 K, elucidating the interplay between structural phase transformations and thermal properties in a solid state superionic conducting material. At room temperature, the thermal conductivity decreases by more than 75% as the stoichiometry is changed from lithium deficient to excess and increases with increasing temperature, indicative of defect-mediated transport in the spark plasma sintered materials. The phase transformations and their stabilities are examined by x-ray diffraction and differential scanning calorimetry and indicate that the Y3+ substitution of Zr4+ is effective in stabilizing the ionically conductive rhombohedral phase over the entire temperature range measured, the mechanism of which is found through ab initio theoretical calculations. These insights into thermal transport of LZP superionic conductors are valuable as they may be generally applicable for predicting material stability and thermal management in the ceramic electrolyte of future all-solid-state-battery devices.

Data obtained from computational studies are crucial in building the necessary infrastructure for materials informatics. This computational foundation supplemented with experimental observations can then be employed in the extraction of possible hidden structure–property relationships through machine learning. There are limited attempts to sample the materials configuration space, even for the simplest chemical formulas. Advances in computational methods have now made it possible to accomplish this task. In this study, we analyze four chemical formulas, i.e., BSb, AlSb, MgSi2, and Sn3S, using first-principles computations. We show that numerous thermodynamically more stable crystal structures can be predicted computationally for these relatively simple chemical formulas, while the configuration space can be significantly and effectively mapped out. This approach allows for the prediction of new ground state structures, thereby expanding the available data on these materials. It also provides an understanding of the underlying potential energy topography and adds quality data for materials informatics.

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First-principles computations are the driving force behind numerous discoveries of hydride-based superconductors, mostly at high pressures, during the last decade. Machine-learning (ML) approaches can further accelerate the future discoveries if their reliability can be improved. The main challenge of current ML approaches, typically aiming at predicting the critical temperature Tc of a solid from its chemical composition and target pressure, is that the correlations to be learned are deeply hidden, indirect, and uncertain. In this work, we showed that predicting superconductivity at any pressure from the atomic structure is sustainable and reliable. For a demonstration, we curated a diverse dataset of 584 atomic structures for which λ and ω log , two parameters of the electronphonon interactions, were computed. We then trained some ML models to predict λ and ω log , from which Tc can be computed in a post-processing manner. The models were validated and used to identify two possible superconductors whose Tc 10 − 15K and zero pressure. Going forward, this strategy will be improved to better contribute to the discoveries of new superconductors.