Can Neural Networks Learn Atomic Stick–Slip Friction?

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.