Can Neural Networks Learn Nanoscale Friction?

TL;DR

This paper demonstrates how neural networks can be trained on synthetic nanofriction force traces to accurately infer physical parameters from experimental data, streamlining interpretation and reducing manual effort.

Contribution

It introduces a machine learning approach using neural networks trained on simulated data to interpret nanofriction force traces and extract underlying physical parameters.

Findings

Neural networks can effectively learn PT model parameters from synthetic force traces.

Proper data manipulation improves the accuracy of parameter inference.

Application to real AFM data validates the method's practical utility.

Abstract

Current nanofriction experiments on crystals, both tip-on-surface and surface-on-surface, provide force traces as their sole output, typically exhibiting atomic size stick-slip oscillations. Physically interpreting these traces is a task left to the researcher. Historically done by hand, it generally consists in identifying the parameters of a Prandtl-Tomlinson (PT) model that best reproduces these traces. This procedure is both work-intensive and quite uncertain. We explore in this work how machine learning (ML) could be harnessed to do that job with optimal results, and minimal human work. A set of synthetic force traces is produced by PT model simulations covering a large span of parameters, and a simple neural network (NN) perceptron is trained with it. Once this trained NN is fed with experimental force traces, it will ideally output the PT parameters that best approximate them. By following this route step by step, we encountered and solved a variety of problems which proved most instructive and revealing. In particular, and very importantly, we met unexpected inaccuracies with which one or another parameter was learned by the NN. The problem, we then show, could be eliminated by proper manipulations and augmentations operated on the training force traces, and that without extra efforts and without injecting experimental informations. Direct application to the sliding of a graphene coated AFM tip on a variety of 2D materials substrates validates and encourages use of this ML method as a ready tool to rationalise and interpret future stick-slip nanofriction data.