Shear Deformation of DLC Based on Molecular Dynamics Simulation and Machine Learning

Shear deformation mechanisms of diamond-like carbon (DLC) are commonly unclear since its thickness of several micrometers limits the detailed analysis of its microstructural evolution and mechanical performance, which further influences the improvement of the friction and wear performance of DLC. This study aims to investigate this issue utilizing molecular dynamics simulation and machine learning (ML) techniques. It is indicated that the changes in the mechanical properties of DLC are mainly due to the expansion and reduction of sp3 networks, causing the stick-slip patterns in shear force. In addition, cluster analysis showed that the sp2-sp3 transitions arise in the stick stage, while the sp3-sp2 transitions occur in the slip stage. In order to analyze the mechanisms governing the bond breaking/re-formation in these transitions, the Random Forest (RF) model in ML identifies that the kinetic energies of sp3 atoms and their velocities along the loading direction have the highest influence. This is because high kinetic energies of atoms can exacerbate the instability of the bonding state and increase the probability of bond breaking/re-formation. Finally, the RF model finds that the shear force of DLC is highly correlated to its potential energy, with less correlation to its content of sp3 atoms. Since the changes in potential energy are caused by the variances in the content of sp3 atoms and localized strains, potential energy is an ideal parameter to evaluate the shear deformation of DLC. The results can enhance the understanding of the shear deformation of DLC and support the improvement of its frictional and wear performance.