Tailoring Frictional Properties of Surfaces Using Diffusion Models

Abstract

This work introduces an approach for precisely designing surface friction properties using a conditional generative machine learning model, specifically a denoising diffusion probabilistic model (DDPM). We created a dataset of synthetic surfaces with frictional properties determined by molecular dynamics simulations, which trained the DDPM to predict surface structures from desired frictional outcomes. Unlike traditional trial-and-error and numerical optimization methods, our approach directly yields surface designs meeting specified frictional criteria with high accuracy and efficiency. This advancement in material surface engineering demonstrates the potential of machine learning to reduce the iterative nature of surface design processes. Our findings provide a pathway for tailoring surface properties and suggest broader applications in materials science where surface characteristics are critical.