Speaker
Description
Transition metal dichalcogenides (TMDs) on metallic substrates, such as Au, exhibit unique interfacial interactions that influence their structural and tribological properties. In this study, we developed a machine learning-based force field using Deep Potential Molecular Dynamics (DPMD) to model TMDs on an Au substrate and the Si tip. The generated force field was validated by computing the radial distribution function (RDF) scalar products of density functional theory (DFT) and DPMD results. Our analysis shows a strong agreement between the DPMD and DFT calculations, demonstrating the accuracy and reliability of the developed force field. The phonon calculations show that all the systems are dynamically stable. These findings contribute to the efficient modelling of TMD-metal interfaces, paving the way for advanced simulations in nanotribology and material design.
