Capturing the Dichotomous Behaviors of Asperity Wear with Neural-Network Surrogate Modeling

We explore the use of neural networks as surrogate models for atomistic simulations of rough surface sliding. While molecular dynamics (MD) simulations provide valuable insights into the tribological behavior of rough surfaces at the asperity level, their high computational cost limits their scalability, making it challenging to analyze macroscale phenomena arising from microscale interactions. To address this limitation, a neural network-based surrogate model is developed to replace the physics solver in atomistic simulations. Our approach involves representing the state of MD simulations as grid-based images containing occupancy and virial stress information, which serve as input to a U-Net-type neural network trained to predict the next simulation state. See manuscript for the end of the abstract.