Spin-flip linear-response time-dependent densityfunctional theory (SF-TDDFT) has been proposed as a cost-effective way of capturing multiconfigurational correlation effects in the groundand excited states. In the usual formulation, the reference density is obtained from a Kohn-Sham (KS) wavefunction for the lowest open-shell triplet (M s = +1), from which response functions are expanded in the basis of spin-flip excitations (∆M s = ±1). Despite some successful examples for describing ground-and excited-state conical intersections, the spin contamination of electronic states described by configurations outside the open-shell orbitals limited its applications. This problem has been recently solved by formulating a spin-flip approach on a mixed-reference triplet of M s = ±1, a method dubbed mixedreference spin-flip TDDFT (MRSF-TDDFT). It nearly eliminates the spin contamination of SF-TDDFT by expanding the electron configuration space while still maintaining a favorable computational cost. Efficient implementation of spin-flip methodologies for High-Performing Computing (HPC) have been recently presented in a new electronic structure software called OpenQP, in an attempt to accelerate discovery of photochemical reactions via semi-classical non-adiabatic dynamics (NAMD). Still, there is a need for speed-up for calculations of ground-and excited-state properties, for performing NAMD at longer timescales or for larger moleculer systems. To this end, semiempirical methodologies offer a good trade-off between accuracy and efficiency. In particular, density functional-based tight-binding (DFTB) has been shown to be particularly accurate for ground and excited state properties, as well as extensions to clusters , QM/MM, and periodic DFTB enhanced by machine learning , etc.