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Description
We investigate the nanoscale friction behaviour of MX$_2$ monolayers (M = Mo, W; X = S, Se) on Au(111) and Ag(111) substrates with a silicon tip using classical molecular dynamics simulations with machine-learning-based force fields. This approach enables an accurate description of tip-surface interactions and friction mechanisms at the atomic scale. We observe a pronounced nonmonotonic dependence of the friction force on the applied normal load, with a corresponding coefficient of friction inversely proportional to the load. Analysis of lateral force signals and their spatial Fourier transforms reveals the coexistence of multiple sliding modes, including longitudinal sliding, lateral slip, and zig-zag motions. We show that the overall friction response is governed by the relative contributions of these motions. While the qualitative features of friction are largely substrate-independent, both the magnitude of friction and the balance between sliding modes depend sensitively on the substrate-monolayer combination. In particular, the Au/MoSe$_2$/Si system exhibits significantly reduced friction due to suppression of lateral slip motion. The analysis method employed in this study is also applicable to other material systems with different chemical compositions or sliding interface geometries. However, the peak positions and intensities are expected to vary with the chemical composition of the material, and consequently, their interpretation may differ across different systems.