Neural Network Potentials for Twisted Few-Layer Materials

Few-layer materials twisted at small angles are experimentally shown to host non-trivial electronic structure such as superconductivity or strongly-correlated insulating state. It is also theoretically demonstrated that in twisted bilayer graphene, the prototypical system for this class of materials, characteristics of the band structure is significantly affected by the atomic relaxation that occurs due to the twist. Since small twist angles result in large Moiré patterns, atomistic simulation of these systems from first principles is computationally costly, and empirical potentials are needed. However, the development of such interatomic potentials is also challenging due to the very different energy and length scales of inter- and intralayer interactions. Furthermore, the changes in atomic positions and energy due to the twist is only a small fraction of the typical length and energy scales, imposing a tight accuracy requirement on the potential design. In this talk we present our attempt at developing such a potential via neural networks, and how the challenges highlighted above translate into practical steps during training and testing. Finally we examine the performance of the neural network potential and its transferability.