| Abstract Scope |
Recent advances in data driven computing leverage artificial intelligence and machine learning techniques to solve diverse challenges involved 2D material. Microscopic and spectroscopic data are involved in complete life cycle of 2D material such as design, discovery. characterization, maintenance, etc. This review uses PRISMA as a systemic review process to ensure relevance and reproducibility. Articles are searched in three publication databases (WoS, PubMed, Dimensions) with two key research questions. PRISMA identifies 31 articles relevant to the topic. The most studied questions are 2D new material synthesis and engineering with reasonable appreciation for functional discovery/property, defect characterization and grain boundary, but work on corrosion application/detection is not done yet as per our analysis. From the relevant set of articles, the tasks prediction, classification, deep learning, clustering, and AI appeared 38.98%, 28.81%, 13.56%, 10.17%, and 8.47% respectively. Results show that AI (i.e., 8.47%) is still an underexplored computing technique. |