Data-driven prediction of phase formation in graphene-metal systems based on phase diagram insights

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.

Keywords

CALPHAD / graphene-metal composite / machine learning / phase formation rule / phase diagram

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