Improving the stability of porous materials for practical applications is highly challenging. Aluminosilicate zeolites are utilized for adsorptive and catalytic applications, wherein they are sometimes exposed to high-temperature steaming conditions (∼1000 °C). As the degradation of high-silica zeolites originates from the defect sites in their frameworks, feasible defect-healing methods are highly demanded. Herein, we propose a method for healing defects to create extremely stable high-silica zeolites. High-silica (SiO2/Al2O3 > 240) zeolites with *BEA-, MFI-, and MOR-type topologies could be stabilized by significantly reducing the number of defect sites via a liquid-mediated treatment without using additional silylating agents. Upon exposure to extremely high temperature (900–1150 °C) steam, the stabilized zeolites retain their crystallinity and micropore volume, whereas the parent commercial zeolites degrade completely. The proposed self-defect-healing method provides new insights into the migration of species through porous bodies and significantly advances the practical applicability of zeolites in severe environments.

Here we provide a database of simulated carbon K (C-K) edge core loss spectra of 117,340 symmetrically unique sites in 22,155 molecules with no more than eight non-hydrogen atoms (C, O, N, and F). Our database contains C-K edge spectra of each carbon site and those of molecules along with their excitation energies. Our database is useful for analyzing experimental spectrum and conducting spectrum informatics on organic materials.

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Artificial neural networks are applied to quantify the properties of organic molecules by introducing a new descriptor, a core‐loss spectrum, which is typically observed experimentally using electron or X‐ray spectroscopy. Using the calculated C K‐edge core‐loss spectra of organic molecules as the descriptor, the neural network models quantitatively predict both intensive and extensive properties, such as the gap between highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) (HOMO–LUMO gap) and internal energy. The prediction accuracy estimated by the mean absolute errors for the HOMO–LUMO gap and internal energy is 0.205 and 97.3 eV, respectively, which are comparable with those of previously reported chemical descriptors. This study indicates that the neural network approach using the core‐loss spectra as the descriptor has the potential to deconvolute the abundant information available in core‐loss spectra for both prediction and experimental characterization of many physical properties. The study shows the practical potential of machine‐learning‐based material property measurements taking advantage of experimental core‐loss spectra, which can be measured with high sensitivity, high spatial resolution, and high temporal resolution.

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