Solid State Chemistry
Solid-state chemistry using quantum chemistry employs advanced computational methods to analyze the electronic structure and behavior of atoms and molecules within solid materials, aiding in the understanding and prediction of their properties and reactivity at the quantum level.
Machine learning plays a crucial role in theoretical chemistry by enabling the development of predictive models that can efficiently analyze complex chemical data, leading to insights, accelerating material discovery, and aiding in the design of novel molecules and materials.
Exchange - Correlation Development
Implementation of xc-functionals in Turbomole for a better description of electronically excited states in materials and molecules that are of interest in energy conversion