Simulation of lithium hydroxide decomposition using deep potential molecular dynamics

Chemical reactions and vapor-liquid equilibria for molten lithium hydroxide (LiOH) were studied using molecular dynamics simulations and a deep potential (DP) model. The neural network for the model was trained on quantum density functional theory data for a range of conditions. The DP model allows simulations over timescales of hundreds of ns, which provide equilibrium compositions for the systems of interest. Single-phase NPT simulations of the liquid show the decomposition of LiOH into lithium oxide (Li2O) and dissolved water (H2O). These DP results were validated by direct ab initio molecular dynamics simulations that confirmed the accuracy of the model with respect to reaction kinetics and equilibrium properties of the melt. The reactive vapor-liquid behavior of this system was subsequently studied using direct coexistence interfacial DP simulations. Partial pressures of H2O in the vapor are found to be in close agreement with available experimental measurements. By fitting temperature-dependent expressions for the reaction equilibrium and Henry's law constants, the equilibrium composition for any given initial composition and temperature can be quantitatively modeled. For high initial concentrations of Li2O or H2O, mixtures of LiOH + Li2O/H2O are found to undergo phase separation. The present study illustrates how DP-based molecular dynamics simulations can be used for quantitative modeling of multiphase reactive behavior with the accuracy of the underlying ab initio quantum chemical methods.