General force fields offer a computationally efficient alternative to quantum mechanical (QM) calculations, in particular for pharmaceutical and biomolecular applications. Parametrizing general force fields or training machine learning (ML) potentials, for molecular mechanics (MM), requires extensive quantum chemistry datasets, with molecules covering a large chemical space, that inform the bonded parameters, typically bonds, angles, and torsions as well as nonbonded terms. The datasets include optimized chemical structures of different conformers of molecules and associated properties such as charges, bond orders, dipole moments, Hessian matrices, and torsion energy profiles for rotatable bonds, dimer interaction energies, among other data. A quantum mechanical method is needed that is globally accurate for different chemistries and properties, is computationally cost effective, and can converge with a predefined set of hyperparameters (or a template with minimal changes) without too much human intervention, thus enabling automated generation of larger datasets essential for this effort. There are hundreds of density functionals, post Hartree-Fock methods, and basis sets to choose from, and it is cumbersome to pick one without a proper benchmark study.