Transition metal dichalcogenides (TMDs) are being actively studied in nextgeneration semiconductor applications owing to their excellent optoelectronic properties. Therefore, numerous defect-related studies have been conducted to improve TMD quality. In the study of defects, Raman spectroscopy is widely used to obtain information regarding the defects on a surface. A single sulfur-vacancy-induced Raman peak was recently reported. However, the origin of this vibrational mode has not yet been identified. Therefore, quantum mechanical calculations were performed on the sulfur-vacancy-containing supercell structure to elucidate the origin. By calculating the band structure and phonon dispersion, the phonon momentum was obtained, considering the possible scattering of electrons. After comparing the phonon momentum and phonon dispersion, it was identified that the phonon vibrational origin of a single sulfurvacancy-induced Raman peak is A′ 1 (k).

No abstract

The application of explainable artificial intelligence in nanomaterial research has emerged in the past few years, which has facilitated the discovery of novel physical findings. However, a fundamental question arises concerning the physical insights presented by deep neural networks; the model interpretation results have not been carefully evaluated. Herein, explainable artificial intelligence and quantum mechanical calculations is bridged to investigate the correlation between light scattering and emission in a WSe2 monolayer. Convolutional neural networks using light scattering and emission data are first trained, while expecting the networks to determine the relationships between them. The trained models are interpreted and the specific phonon contribution during the exciton relaxation process is derived. Finally, the findings are independently evaluated through quantum mechanical calculations, such as the Born–Oppenheimer molecular dynamics simulation and density functional perturbation theory. The study provides reliable fundamental physical insight by evaluating the results of neural networks and suggests a novel methodology that can be applied in materials science.