  This Letter introduces an approach for precisely designing surface friction
properties using a conditional generative machine learning model, specifically
a diffusion denoising 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 in reducing the iterative nature of surface design processes.
Our findings not only provide a new pathway for precise surface property
tailoring but also suggest broader applications in material science where
surface characteristics are critical.
