High throughput calculations for a dataset of bilayer materials

Frictional Properties of Two-Dimensional Materials: Data-Driven Machine Learning Predictive Modeling

Friction, typically associated with reduced efficiency and reliability of machines and devices, occurs when two objects are displaced against each other. This is a strongly material-dependent phenomenon, and the emergence of many 2D materials has opened up new opportunities to design systems with desired tribological properties. Here, we combine high throughput simulations and machine learning models to develop a statistical approach of adhesion, van der Waals, and corrugation energies of a large dataset of monolayered materials. The machine learning models are used to predict these closely related to friction energetic properties and link them to easily accessible atomistic and monolayer features. This approach elevates the materials' perspective of frictional properties. It demonstrates that data-driven models are extremely useful in discovering important structure-property functionalities for frictional property interpretations as a fruitful route toward desired tribological materials.

Engineering piezoelectricity at vdW interfaces of quasi-1D chains in 2D Tellurene

Low-dimensional piezoelectrics have drawn attention to the realization in nano-scale devices with high integration density. A unique branch of 2D Tellurene bilayers formed of weakly interacting quasi-1D chains via van der Waals forces is found to exhibit piezoelectricity due to the semiconducting band gap and spatial inversion asymmetry. Various bilayer stackings are systematically examined using density functional theory, revealing optimal piezoelectricity when dipole arrangements are identical in each layer. Negative piezoelectricity has been observed in two of the stackings AA' and AA″ while other two stackings exhibit the usual positive piezoelectric effect. The layer-dependent 2D piezoelectricity (∣e222D ∣) increases with an increasing number of layers in contrast to the odd-even effect observed in h-BN and MoS2. Notably, the piezoelectric effect is observed in even-layered structures due to the homogeneous stacking in multilayers. Strain is found to enhance in-plane piezoelectricity by 4.5 times (-66.25 × 10-10C m-1at -5.1% strain) due to the increasing difference in Born effective charges of positively and negatively charged Te-atoms under compressive biaxial strains. Moreover, out-of-plane piezoelectricity is induced by applying an external electric field, thus implying Tellurene is a promising candidate for piezoelectric sensors.