Ab initio-driven machine-learning models for aqueous systems, interfaces, and molten salts

This presentation focuses on recently developed machine-learning models based on ab initio quantum chemical methods. These allow simulations of aqueous systems, interfaces, and molten salts over time and length scales previously reachable only for classical force fields developed by fitting their macroscopic properties. In particular, we take advantage of the Deep Potential (DeePMD) methodology to capture energies and forces in a given system, as determined from quantum density functional theory. The DeePMD approach has the advantage that a well-developed software framework is available for rapid training of the relevant models and efficient implementation within standard open-source molecular dynamics codes. Example applications are presented here for water’s vapor-liquid and liquid-liquid phase behavior, fluid-phase properties of CO₂, and properties of aqueous electrolyte solutions that are hard to obtain accurately using empirical force fields. In addition, we investigate rates for bubble cavitation and homogeneous ice nucleation [7]. Further illustrations of the power of the DeePMD approach are provided by ongoing work on heterogeneous ice formation on microcline feldspar and the properties of carbonate and hydroxide melts relevant for high-temperature fuel cell operation. Ab initio-based machine learning models are shown to have excellent predictive capabilities for thermodynamic and transport properties of solutions and melts with no need for input from experimental data, they automatically include multibody, polarizability and charge transfer effects, and naturally describe chemical reactions. However, they are limited in accuracy by the underlying quantum chemical methods. They have complex, physically opaque model structures and require additional training for extending to mixtures. Despite these disadvantages, they likely represent the future of molecular modeling at the atomistic level of detail.