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Point defects, dislocations, grain boundaries and interfaces are always involved in ceramic microstructures and play important roles for physical and chemical properties of ceramics. Currently, proper control of these crystal defects is inevitable to tailor ceramic materials with superior properties. This article reviews recent research projects on distinct properties and phenomena in ceramics due to crystal defects. In particular, we would like to emphasize importance of central core regions of crystal defects, namely, "crystal defect cores". They have specific electronic and atomic structures that are different from those in bulk. Recent advances of nanoscale characterizations and theoretical calculations make it possible to acquire a variety of quantitative data on electronic structures enclosed at the crystal-defect cores, which gives clear understanding of various ceramic properties at the electronic and atomic levels.

The discovery of molecules with specific properties is crucial to developing effective materials and useful drugs. Recently, to accelerate such discoveries with machine learning, deep neural networks (DNNs) have been applied to quantum chemistry calculations based on the density functional theory (DFT). While various DNNs for quantum chemistry have been proposed, these networks require various chemical descriptors as inputs and a large number of learning parameters to model atomic interactions. In this paper, we propose a new DNN-based molecular property prediction that (i) does not depend on descriptors, (ii) is more compact, and (iii) involves additional neural networks to model the interactions between all the atoms in a molecular structure. In the consideration of the molecular structure, we also model the potentials between all the atoms; this allows the neural networks to simultaneously learn the atomic interactions and potentials. We emphasize that these atomic "pair" interactions and potentials are characterized using the global molecular structure, a function of the depth of the neural networks; this leads to the implicit or indirect consideration of atomic "many-body" interactions and potentials within the DNNs. In the evaluation of our model with the benchmark QM9 data set, we achieved fast and accurate prediction performances for various quantum chemical properties. In addition, we analyzed the effects of learning the interactions and potentials on each property. Furthermore, we demonstrated an extrapolation evaluation, i.e., we trained a model with small molecules and tested it with large molecules. We believe that insights into the extrapolation evaluation will be useful for developing more practical applications in DNN-based molecular property predictions.

With the examples of the C K-edge in graphite and the B K-edge in hexagonal BN, we demonstrate the impact of vibrational coupling and lattice distortions on the X-ray absorption near-edge structure (XANES) in 2D layered materials. Theoretical XANES spectra are obtained by solving the Bethe-Salpeter equation of many-body perturbation theory, including excitonic effects through the correlated motion of core-hole and excited electron. We show that accounting for zero-point motion is important for the interpretation and understanding of the measured X-ray absorption fine structure in both materials, in particular for describing the σ * -peak structure.PACS numbers: