Molecular Dynamics Simulations of $\gamma$-Belite(010)-Water Interfaces with High-Dimensional Neural Network Potentials

TL;DR

This study uses high-dimensional neural network potentials trained on DFT data to simulate water interactions with $eta$-belite surfaces, revealing surface reactivity, defect formation, and stabilization mechanisms relevant to low-carbon cement.

Contribution

It introduces a neural network potential for accurate MD simulations of belite-water interfaces, providing new insights into surface stability and defect dynamics.

Findings

Water interacts in molecular and dissociative forms with the surface.

Surface defects influence reconstruction and stability.

Water presence stabilizes various surface structures.

Abstract

Belite -- dicalcium silicate Ca$_2$SiO$_4$ -- is a main constituent of low-carbon cement. In this work, we study several terminations of the (010) surface of $\gamma$-belite, its most stable polymorph, by molecular dynamics simulations. The energies and forces are provided by a high-dimensional neural network potential trained to density functional theory data. Water can interact in molecular form as well as dissociatively with the investigated interfaces, and the degree of dissociation is determined primarily by the protonation of SiO$_4$ groups accessible at the surface. A major part of the simultaneously formed hydroxide ions is adsorbed at surface calcium atoms, whose octahedral coordination spheres are completed by additional water molecules. The T3 termination, which is most stable in vacuum, shows only little reactivity in water. For the only slightly less stable T2 termination, however, two distinct types of surface defects are observed. The type I defect is even stable in vacuum and leads to a reconstruction of the entire surface, while the type II defect is only found in the presence of water. Overall, our results suggest that a variety of structures may be formed at the Ca$_2$SiO$_4$(010) surface, which are stabilized in the presence of water.