Computational Prediction of Superlubric Layered Heterojunctions.

Structural superlubricity has attracted increasing interest in modern tribology. However, experimental identification of superlubric interfaces among the vast number of heterojunctions is a trial-and-error and time-consuming approach. In this work, based on the requirements on the in-plane stiffnesses of layered materials and the interfacial interactions at the sliding incommensurate interfaces of heterojunctions for structural superlubricity, we propose criteria for predicting structural superlubricity between heterojunctions. Based on these criteria, we identify 61 heterojunctions with potential superlubricity features from 208 candidates by screening the data of first-principles calculations. This work provides a universal route for accelerating the discovery of new superlubric heterojunctions.