Since the discovery of graphene, 2D materials research has been a growing and active community due to the powerful properties of some of these low dimensional materials. Due to the difficulty of synthesis of these materials, computational tools are typically used to predict what monolayers will be stable enough to synthesize. Recently, data mining has been used to identify over 800 synthesizable 2D materials. This provides a rich database to use with machine learning tools to predict exciting new monolayers using discovery methods such as chemical substitution.
Back to Complex High-Dimensional Energy Landscapes