Data‐driven prediction of phase formation in graphene–metal systems based on phase diagram insights - Leilei Chen; Changheng Li; Kai Xu; Ruonan Zhou; Ming Lou; Yujie Du; Denis Music; Keke Chang (Materials Genome Engineering Advances)
Abstract Graphene–metal (G‐M) composites have attracted tremendous interests due to their promising applications in electronics, optics, energy‐storage devices and nano‐electromechanical systems. Especially, phase formations of graphene combined with different metals are considered valuable for discovering and designing advanced G‐M composites. However, the phase formations in G‐M systems have rarely been systematically described since graphene was first extracted from graphite in 2004. Here, we propose a data‐driven approach to predict the phase formations in G‐M systems leveraging G‐M binary phase diagrams, which were established using the calculation of phase diagrams method. Phase relationships obtained from G‐M phase diagrams of 34 systems and formation enthalpies of corresponding carbides were employed as the training dataset in a machine learning model to further predict the phase formations in additional 13 G‐M systems. Phase formation predictions achieved an accuracy of 87.5% in the test dataset. Three distinct phase formations were characterised in G‐M systems. Finally, we propose a general phase formation rule in the G‐M systems: metals with smaller atomic numbers in the same period are more likely to form secondary solutions with graphene.