Speaker
Description
In this work, we study the tribological response of transition metal dichalcogenide monolayers interacting with a metallic substrate and a silicon atomic force microscopy tip. High-quality machine learning force fields [1,2] are developed and subsequently used to perform classical molecular dynamics (MD) simulations at room temperature, to investigate the nanofriction behavior under varying applied loads and sliding velocities of the Si tip. The resulting MD trajectories are then analysed in terms of renormalized phonon, eigendisplacements, eigenfrequencies and relative atomic participation.
This analysis provides detailed insight into how vibrational modes, such as sliding and breathing modes, determine the friction response and what is the relative role of substrate, monolayer and tip. The results provide information on how to tune the friction response by proper selection of the kind of substrate and monolayer. This analysis is general and can be promptly applied to the study of frictional interfaces irrespective of the chemical composition and atomic topology.
Keywords: machine learning force fields, transition metal dichalcogenides, nanofriction, phonon analysis, molecular dynamics, heterostructure
References
[1] Shaidu et al. npj Computational Materials 2025, 11, 273
[2] Denghui Lu et al. Computer Physics Communications 2021, 259, 107624