Excited State

Excited states represent the energy levels of atoms and molecules above their ground state, and their accurate prediction is crucial for understanding diverse phenomena in chemistry and physics. Current research focuses on developing computationally efficient methods for calculating excited state properties, employing techniques like machine learning (ML) with architectures such as neural networks (including variational Monte Carlo and normalizing flows) and multi-fidelity models to improve accuracy and reduce computational cost. These advancements are significantly impacting fields ranging from materials science (designing new phosphors and semiconductors) to chemical analysis (improving spectroscopic data interpretation) by enabling faster and more accurate simulations of complex systems.