Ceramic materials are polycrystalline solids that are made up of metal and non-metal
elements, and inorganic crystal grains with specific crystal structures are fundamental …

Conventional machine-learning (ML) models in computational chemistry learn to directly
predict molecular properties using quantum chemistry only for reference data. While these …

We report a comprehensive computational study of unsupervised machine learning for
extraction of chemically relevant information in X-ray absorption near edge structure …

We analyze an ensemble of organophosphorus compounds to form an unbiased
characterization of the information encoded in their X-ray absorption near-edge structure …

Machine learning methods have enabled the low-cost evaluation of molecular properties
such as energy at an unprecedented scale. While many of such applications have focused …

Derivatives of BODIPY are popular fluorophores due to their synthetic feasibility, structural
rigidity, high quantum yield, and tunable spectroscopic properties. While the characteristic …

The core-loss spectrum reflects the partial density of states (PDOS) of the unoccupied states
at the excited state and is a powerful analytical technique to investigate local atomic and …

Microscopic evaluation is one of the most effective methods in materials research. High-
quality images are essential to analyze microscopic images using artificial intelligence. To …

Electronically excited-state problems represent a crucial research field in quantum
chemistry, closely related to numerous practical applications in photophysics and …

A systematic comparison is demonstrated for the predictions of frontier orbital energies─
highest occupied molecular orbital (HOMO)(EH), lowest unoccupied molecular orbital …