Molecular dynamics of the asymmetric solid–liquid interface

A review of recent advances and applications of machine learning in tribology

In tribology, a considerable number of computational and experimental approaches to understand the interfacial characteristics of material surfaces in motion and tribological behaviors of materials have been considered to date. Despite being useful in providing important insights on the tribological properties of a system, at different length scales, a vast amount of data generated from these state-of-the-art techniques remains underutilized due to lack of analysis methods or limitations of existing analysis techniques. In principle, this data can be used to address intractable tribological problems including structure-property relationships in tribological systems and efficient lubricant design in a cost and time effective manner with the aid of machine learning. Specifically, data-driven machine learning methods have shown potential in unraveling complicated processes through the development of structure-property/functionality relationships based on the collected data. For example, neural networks are incredibly effective in modeling non-linear correlations and identifying primary hidden patterns associated with these phenomena. Here we present several exemplary studies that have demonstrated the proficiency of machine learning in understanding these critical factors. A successful implementation of neural networks, supervised, and stochastic learning approaches in identifying structure-property relationships have shed light on how machine learning may be used in certain tribological applications. Moreover, ranging from the design of lubricants, composites, and experimental processes to studying fretting wear and frictional mechanism, machine learning has been embraced either independently or integrated with optimization algorithms by scientists to study tribology. Accordingly, this review aims at providing a perspective on the recent advances in the applications of machine learning in tribology. The review on referenced simulation approaches and subsequent applications of machine learning in experimental and computational tribology shall motivate researchers to introduce the revolutionary approach of machine learning in efficiently studying tribology.

Three-Dimensional Structure of a Simple Liquid at a Face-Centered-Cubic (001) Solid Surface Interface

A liquid in the vicinity of a solid-liquid interface (SLI) may exhibit complex structures. In this study, we used molecular dynamics simulations demonstrating for the first time that the liquid adjacent to the SLI can have a two-level structure in some cases: a major structure and a minor structure. Through a time-averaging process of molecular motions, we identified the type of the liquid structure by calculating positions of the maximum liquid density in three spatial dimensions, and these positions were found to distribute in many dispersed zones (called high-density zones (HDZs)). The major structure appears throughout the SLI, while the minor structure only occurs significantly within the third layer. Instead of the previously reported body-centered cubic (BCC) or face-centered-cubic (FCC) types, the major structure was found to show a body-centered tetragonal (BCT) type. The adjacent HDZs are connected by specific junctions, demonstrating that atoms diffuse along some particular high probability paths from one HDZ to another. By considering the three-dimensional liquid density distribution from the continuum point of view, more complete details of the structure and diffusive behavior of liquids in the SLI are also possible to be revealed.