Symmetry-informed high-throughput search for domain walls in 2D materials

Defects and domain walls in 2D materials can host many novel quantum states determined by the atomic structure of those defects. However, comprehensively exploring the potential structures of these domain walls is challenging. We present a symmetry-informed approach to discover new possible domain walls. Our newly-developed symmetry tables provide a recipe for generating candidate configurations, and universal machine-learning potentials allow for rapid screening of candidates for stability. The electronic and quantum states of the best candidates are inspected by density functional theory. This work helps uncover new material platforms for quantum nanomaterials.