Speaker
Description
Transition metal dichalcogenides (TMDs) are a class of layered materials in which weak interlayer van der Waals forces enable facile sliding, giving rise to an exceptionally low coefficient of friction which effectively vanishes (< 10$^{-3}$) when measured in vacuum. In humid environments, however, frictional properties degrade due to oxidisation at the sliding interface, which increases friction (~10$^{-1}$) and accelerates wear. Such degradation can be mitigated through doping with selected cations or anions, and/or by employing multilayer heterostructure design, both of which increase hardness and thus enhance the performance and lifetime of the low-friction state. Despite these advances, a fundamental understanding of the atomic-scale mechanisms that govern friction in TMDs remains limited, hindering the rational design of lubricants with tailored properties. To tackle this issue, our plan is to systematically investigate doped and heterostructured TMDs to understand how frictional energy dissipation arises from phonon-phonon scattering, and subsequently how it can be tuned via materials design. Here, we present the first steps along this path: we setup and benchmark simulations of phonon modes and phonon-phonon scattering in doped and heterostructured TMDs using a machine-learning approach. The accuracy of the method is demonstrated through convergence of lattice thermal conductivity calculations, providing a validated framework that will later be combined with analysis of phonon-phonon scattering to link atomic-scale phonon-based energy dissipation to macroscopic tribological performance.
This work is co-funded by the European Union under the project “Robotics and advanced industrial production” (reg. no. CZ.02.01.01/00/22_008/0004590), the Czech Science Foundation project “Superlubricity: Sliding of 2D Materials” No. 23-07785S, and by the Ministry of Education, Youth and Sports of the Czech Republic through e-INFRA CZ: (ID: 90254).
