The data-intensive scientific revolution occurring where two-dimensional materials meet machine learning

Machine learning (ML) has experienced rapid development in recent years and been widely applied to assist studies in various research areas. Two-dimensional (2D) materials, due to their unique chemical and physical properties, have been receiving increasing attention since the isolation of graphene. The combination of ML and 2D materials science has significantly accelerated the development of new functional 2D materials, and a timely review may inspire further ML-assisted 2D materials development. In this review, we provide a horizontal and vertical summary of the recent advances at the intersection of the fields of ML and 2D materials, discussing ML-assisted 2D materials preparation (design, discovery, and synthesis of 2D materials), atomistic structure analysis (structure identification and formation mechanism), and properties prediction (electronic properties, thermodynamic properties, mechanical properties, and other properties) and revealing their connections. Finally, we high-light current research challenges and provide insight into future research opportunities.

Automated summary

The combination of machine learning and 2D materials science has accelerated the development of new functional 2D materials, with discussions on how machine learning assists in the preparation, atomic structure analysis, and property prediction of 2D materials, revealing the connections between the two fields.