Unravelling the dissolution dynamics of silicate minerals by deep learning molecular dynamics simulation: A case of dicalcium silicate

Abstract

Quantitative analyses of the thermodynamics and kinetics of silicate minerals dissolution at atomic level are difficult currently, both experimentally and computationally. Here, we apply the deep learning, enhancing sampling molecular dynamics and density functional theory to build up a deep neural network potential with quantum mechanics accuracy, which can determine the free energy surfaces, minimum free-energy reaction pathways and kinetic rates for Ca dissolution from the water/dicalcium silicate interface at different temperatures. We find that the Ca dissolution is a spontaneous reaction and follows different minimum free-energy reaction pathways at different temperatures. The dissolution time of the five-coordinated Ca ion is on the order of hundreds of seconds at ambient temperature and increases to the order of nanoseconds after heating. The relatively slow dissolution kinetics comparing to the low free energy barriers of each elementary reaction is attributed to the multi-directional and multi-step nature of the dissolution reaction. This new atomistic insight promotes a better understanding of the dicalcium silicate and cement hydration.