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 various research areas. Two-dimensional (2D) materials, due their unique chemical physical properties, have receiving increasing attention since the isolation of graphene. The combination ML 2D materials science significantly accelerated new functional a timely review may inspire further ML-assisted development. In this review, we provide horizontal vertical summary advances at intersection fields discussing preparation (design, discovery, synthesis materials), atomistic structure analysis (structure identification formation mechanism), properties prediction (electronic thermodynamic mechanical other properties) revealing connections. Finally, highlight current challenges insight into future opportunities. machine materials. Yin et al. two fields, covering analysis, property prediction.