Computational Spectroscopy

Computational spectroscopies based on ab initio simulations play a pivotal role in interpreting experimental measurements by providing microscopic physical insights. These insights correlate spectral features to underlying structural, electronic, and magnetic properties of materials. With rapid advancements in theory and the growing integration of machine learning (ML), new opportunities are emerging to develop computational spectroscopy tools that combine physics-based modeling with ML to greatly enhance both fidelity and efficiency. This new paradigm paves the way for real-time spectral analysis in contexts such as autonomous experimentation and materials design. This special issue showcases state-of-the-art theoretical and ML approaches—particularly in Vibrational Spectroscopy, X-ray Spectroscopy, Nuclear Magnetic Resonance (NMR), and UV/Visible Spectroscopy—and highlights their effectiveness in probing the structure and dynamics of complex systems. Target systems include both bulk phases and interfaces.