The Enhancement of Charge Transfer and Friction at the Interfaces between Defected h-BN and Water: a Density-Functional-Theory-Based Machine Learning Study

Abstract

Understanding the friction behavior between hexagonal boron nitride (h-BN) and water is critical for the potential applications of h-BN in liquid-related functional devices. By using a density-functional-theory (DFT)-based machine learning (ML) technique combined with long-time ML-parameterized molecular dynamics simulations, we have systematically investigated charge transfer and friction at the interfaces between h-BN and water. The introduction of defects (including Stone-Wales, B-vacancy, N-vacancy, and B-vacancy/N-vacancy defects) into h-BN significantly enhances heterogeneous charge polarization and distribution at h-BN layers, as well as increases the friction coefficients at water/h-BN interfaces compared to perfect h-BN. The observed increase in interfacial friction of defected h-BN can be attributed to stronger charge transfer and higher charge density at the defected h-BN layers induced by interactions with water molecules. Our results offer deeper insights into the role of defects in modulating charge exchange and transfer between water and h-BN, as well as their impact on interfacial friction.