The primary strength of the quantum mechanics (QM) workflow lies in its flexibility, which facilitates customization through various means, such as simulating different solvent effects or examining the impact of different catalysts and ligands, extending beyond Pd(OAc) 2 . Additionally, accounting for varying reaction conditions, like conducting the reaction under acidic or basic environments, is possible by adjustments to the substrate-SMILES. Protonation states of substrates can be predicted using either machine learning models or QM calculations .