Molecular Simulations of Fluid Interfaces

Fluid interfaces are fundamental to numerous natural and industrial processes, making their study crucial for both academic and practical purposes. Molecular dynamics (MD) simulations have become an indispensable tool for investigating the structures and molecular‐level phenomena occurring at these interfaces. This review explores various computational strategies employed to model fluid interfaces, including classical force fields, quantum mechanical (QM) methods, and neural network potentials. The review begins by discussing the choice of potential energy functions, followed by a discussion of boundary conditions and their importance in simulating systems like the air‐water and water–oil interfaces. The review then shifts to comparing nonpolarizable and polarizable force fields, highlighting when electronic polarization becomes necessary for accurately modeling the interface systems. The use of ab initio molecular dynamics (AIMD) is also examined, particularly for its ability to capture electronic effects, albeit with significant computational costs. Finally, we explore the growing role of machine learning, particularly neural network potentials, in simulating complex interface systems. By reviewing key studies on air‐water and water–oil interfaces, we summarize the latest advancements in modeling fluid interfaces, with particular attention to chemical reactions near these interfaces. This review provides a concise and approachable overview of the computational approaches that are advancing our understanding of fluid interfaces at the molecular scale. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Software > Molecular Modeling Theoretical and Physical Chemistry > Statistical Mechanics