With the development of Raman spectroscopy and the expansion of its application domains,
conventional methods for spectral data analysis have manifested many limitations. Exploring …

Isolated transition metal clusters have been established as useful models for extended metal
surfaces or deposited metal particles, to improve the understanding of their surface …

Abstract Machine learning (ML) and deep learning (DL) possess excellent advantages in
data analysis (eg, feature extraction, clustering, classification, regression, image recognition …

Raman spectroscopy is an important characterization tool with diverse applications in many
areas of research. We propose a machine learning (ML) method for predicting …

Infrared (IR) spectroscopy is a powerful and versatile tool for analyzing functional groups in
organic compounds. A complex and time-consuming interpretation of massive unknown …

Raman spectroscopy is a powerful and nondestructive method that is widely used to study
the vibrational properties of solids or molecules. Simulations of finite-temperature Raman …

Vibrational spectroscopy is a key technique to elucidate microscopic structure and
dynamics. Without the aid of theoretical approaches, it is, however, often difficult to …

Solid-state ion conductors (SSICs) have emerged as a promising material class for
electrochemical storage devices and novel compounds of this kind are continuously being …

Raman spectroscopy is an important tool in the study of vibrational properties and
composition of molecules, peptides, and even proteins. Raman spectra can be simulated …

Atomistic modeling can provide valuable insights into the design of novel heterogeneous
catalysts as needed nowadays in the areas of, eg, chemistry, materials science, and biology …