Transferability of data-driven, many-body models for CO2 simulations in the vapor and liquid phases

Extending on previous work by Riera et al. [J. Chem. Theory Comput. 16, 2246 (2020)], we introduce a second generation family of data-driven many-body MB-nrg models for CO2 and systematically assess how the strength and anisotropy of the CO2-CO2 interactions affect the models' ability to predict vapor, liquid, and vapor-liquid equilibrium properties. Building upon the many-body expansion formalism, we construct a series of MB-nrg models by fitting 1-body and 2-body reference energies calculated at the coupled cluster level of theory for large monomer and dimer training sets. Advancing from the first generation models, we employ the Charge Model 5 scheme to determine the atomic charges and systematically scale the 2-body energies to obtain more accurate descriptions of vapor, liquid, and vapor-liquid equilibrium properties. Comparisons with the polarizable TTM-nrg model, which is constructed from the same training sets as the MB-nrg models but using a simpler representation of short-range interactions based on conventional Born-Mayer functions, showcase the necessity of high dimensional functional forms for an accurate description of the multidimensional energy landscape of liquid CO2. These findings emphasize the key role played by the training set quality and flexibility of the fitting functions in the development of transferable, data-driven models which, accurately representing high-dimensional many-body effects, can enable predictive computer simulations of molecular fluids across the entire phase diagram.