# Can Neural Networks Learn Atomic Stick–Slip Friction?

## TL;DR

This paper shows that neural networks can learn to analyze atomic stick-slip friction data, a task previously done manually.

## Contribution

The first use of a neural network trained only on synthetic data to interpret experimental nanofriction traces.

## Key findings

- A simple neural network successfully extracted PT model parameters from experimental data.
- Incorporating physics-based descriptors improved transferability from synthetic to real data.
- The approach was validated on graphene-coated AFM tips interacting with 2D materials.

## Abstract

Nanofriction experiments
typically produce force traces exhibiting
atomic stick–slip oscillations, which researchers have traditionally
analyzed with ad hoc algorithms. This study successfully unravels
the potential of machine learning (ML) to interpret nanofriction force
traces and automatically extract Prandtl–Tomlinson (PT) model
parameters. A prototypical neural network (NN) perceptron was trained
on synthetic force traces generated by simulations across a wide parameter
range. Despite its simplicity, this NN successfully analyzed experimental
data, marking the first application of a network trained solely on
computational data to experimental nanofriction. Challenges encountered
in developing the NN model proved to be instructive and revealing.
Poor transferability from synthetic to experimental data sets was
resolved by incorporating physics-based descriptors into the synthetic
training data, without experimental input. Our protocol’s simplicity
underscores its proof-of-concept nature, paving the way for advanced
approaches. Validation with experimental data, such as graphene-coated
AFM tips on 2D materials, highlights the promise of this ML approach
for stick–slip nanofriction studies.