Metal-oxo species are the key catalytic species in a number of reactions, including epoxidation, C-H bond activation and water oxidation. Their activity inspires the search new catalysts to address societal challenges in renewable energy storage and sustainable synthesis. For many reactions computational design has become an important tool to identify new catalyst candidates. However, a number of obstacles must be overcome to realize its potential for metal-oxo catalysts: elimination of mechanistic uncertainties, filling of a sparse data space, and proper handling of multiple electronic states. This computational project aims to remove these obstacles through a combination of spectroscopy and reactivity modeling. For multiconfigurational wavefunction calculations of electronic structure and spectra, we will use the novel MultiPsi program. This is a python-driven program designed to run very efficiently on modern HPC environments (M. Delcey, WIRES Comput. Mol. Sci. 13.6 (2023), e1675.) These calculations will be complemented by reactivity modeling using density-functional theory, with center-provided software Gaussian16 and Orca6. One of the targets will be a manganese complex with a novel chiral dicarboxylic ligand that catalyses highly enantio- and position-selective epoxidation of carbon-carbon double bonds under mild conditions (J. Am. Chem. Soc. 2025, 147, 1448−1451). The project will also establish reliable correlations between reactivity and experimental data and remove mechanistic uncertainties. At a longer perspective, these principles can be used to propose novel metal-oxo complexes with tuned reactivity, designed from both synthetic and fully computational platforms.