Machine Learning Potential to Study Phase Transition in 2D TMD material

In Atomistic simulations, the interatomic potential that describes the interactions of atoms determines the fidelity of simulation results. The main disadvantage of the classical interatomic potentials is that they are limited by the fixed functional forms and small number of fitting parameters. As a result, they may not be able to provide reliable predictions. By contrast, machine learning potentials are not relying on a physical functional form, but must learn the physical shape of the energy surface from the training dataset. Therefore, if the training datasets (which usually come from first principle calculations) cover sufficient physics, a well-trained ML potential is able to provide accurate predictions that are comparable to first principle results. <br/> <br/>In this talk, I will present a machine learning potential to study the phase transition in two-dimensional (2D) transition-metal dichalcogenides (TMDs) material, using MoTe₂ as a model system. 2D TMDs such as MoS₂, WS₂, MoSe₂, and WSe₂ present an unprecedented materials family. These materials promise to open up a new age of atomic-scale technology where devices can be scaled down to the truly atomic level and provide novel functionalities that cannot be obtained with conventional materials systems. One of the remarkable features of 2D TMDC is phase transition. Depending on the atom arrangements, 2D TMDC appears in two distinct stable phases: the 2H and 1T′ phases. These two phases exhibit completely different electronic structures, with the 2H phase being semiconducting and the 1T′ phase metallic. The dynamic control of transitions between these two phases can lead to revolutionary device applications.