The research section CMT focuses on modeling the response of materials to tribological loading. The primary goal is to understand how materials behave at the near-surface regions when subjected to friction and wear.

We employ advanced computational methods, particularly large-scale molecular dynamics (MD) simulations, to investigate the microstructural evolution of materials under dry sliding and abrasive conditions, providing insights into deformation mechanisms and mapping the dominant ones as a function of the operating conditions. Additionally, we have used reactive MD simulations to explore the tribological behavior of advanced 2D materials like MXenes, shedding light on the role of surface chemistry in friction and failure processes.

A new focus of the research section’s current work is the integration of machine learning (ML) into tribological modeling. By training models on large, self-consistent datasets generated from MD simulations, we aim to construct “tribological Ashby charts” based on a material’s initial microstructure, providing insights into its performance under various tribological conditions. In the long run, these ML models are expected to replace costly MD simulations for predicting tribological properties, offering a more efficient and engineering-relevant approach for tribological material design and optimization.