Microscopic contributions to the deviation from Amontons friction law

chemistry Medicinal and pharmaceutical chemistry Nano- and molecular-scale electronics Nano-biomaterials and bioscience Nanomagnetics Nanomaterials, thin films and nanointerfaces Nanomedicine Nanometrology and nanomechanics Nano-optics Nanopatterning, self-assembly and nanofabrication Nanostructures for energy and sensing applications Natural products chemistry Organo main group chemistry Other nanotechnology (unclassified) Other organic chemistry (unclassified) Photochemistry and photovoltaics Physical organic chemistry Supramolecular chemistry We investigate the nanoscale friction behaviour of MX2 monolayers (M = Mo, W; X = S, Se) on Au(111) and Ag(111) substrates with 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, indicating a breakdown of Amontons's law at the nanoscale. 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, Au/MoSe2/Si exhibits significantly reduced friction due to suppression of lateral slip motion. Our results indicate that the method is broadly applicable for probing nanoscale friction in related heterostructures.

density functional theory; deep neural network; fourier transform; machine learning force field; molecular dynamics; nanofriction; nonmonotonic; transition metal dichalcogenides | Format: ZIP | Size: 1.1 MB | Download | When a peer-reviewed version of this preprint is available, this information will be updated in the information box above. If no peer-reviewed version is available, please cite this preprint using the following information: Ravisankar, S.; Kumar, R.; Cammarata, A.; Glatzel, T.; Polcar, T. Beilstein Arch. 2026, 202613. doi:10.3762/bxiv.2026.13.v1 Citation data can be downloaded as file using the "Download" button or used for copy/paste from the text window below. Citation data in RIS format can be imported by all major citation management software, including EndNote, ProCite, RefWorks, and Zotero. © 2026 Ravisankar et al.; licensee Beilstein-Institut. This is an open access work licensed under the terms of the Beilstein-Institut Open Access License Agreement (https://www.beilstein-archives.org/xiv/terms), which is identical to the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). The reuse of material under this license requires that the author(s), source and license are credited. Third-party material in this work could be subject to other licenses (typically indicated in the credit line), and in this case, users are required to obtain permission from the license holder to reuse the material.