A Machine Learning Force Field Study of Hydrogen Bonding in Nanoconfined Water
Pavan Ravindra, Columbia University
Recent experiments have shown that water's properties change dramatically when it is confined to nanometer-scale regions. However, we have yet to develop a complete theory for why these property differences occur. Since hydrogen bonding plays a central role in our understanding of bulk water, an essential step towards developing such a theory is to understand the hydrogen bonding behavior of nanoconfined water. In this work, we use machine learning force fields to model a layer of water molecules trapped between two sheets of graphene. We characterize the hydrogen bonding networks of this system across a wide range of temperatures and pressures. Our simulations reveal stark differences in the hydrogen bonding of nanoconfined systems compared to their bulk counterparts. These differences lead to unusual microscopic properties, such as long-range concerted molecular motion. Our results generally suggest that hydrogen bonding plays a reduced role in nanoconfined environments.