A notable strength of MD approaches for thermodynamic predictions is their independence from prior knowledge of aggregate structures, unlike strategies that compute partition functions that necesitation the identification of the relevant minima. Nevertheless, several challenges persist for MD-based approaches. Accurate determination of population distributions requires a dynamic evaluation of chemical identities at each simulation step, which typically relies on geometric criteria that may involve some arbitrariness. Furthermore, for species present at low concentrations, achieving sufficient conformational sampling demands extended simulation times and/or large system sizes. In contrast, first-principles quantum chemistry calculations are well-suited to these scenarios, employing efficient reaction equilibrium algorithms to compute species concentrations without the need for extensive sampling . Still, a comprehensive and reliable framework to compare the performance of