Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning

Despite today's commercial-scale graphene production using chemical vapor deposition (CVD), the growth of high-quality single-layer graphene with controlled morphology and crystallinity remains challenging. Considerable effort is still spent on designing improved CVD catalysts for producing high-quality graphene. Conventionally, however, catalyst design has been pursued using empirical intuition or trial-and-error approaches. Here, we combine high-throughput density functional theory and machine learning to identify new prospective transition metal alloy catalysts that exhibit performance comparable to that of established graphene catalysts, such as Ni(111) and Cu(111). The alloys identified through this process generally consist of combinations of early- and late-transition metals, and a majority are alloys of Ni or Cu. Nevertheless, in many cases, these conventional catalyst metals are combined with unconventional partners, such as Zr, Hf, and Nb. The approach presented here therefore highlights an important new approach for identifying novel catalyst materials for the CVD growth of low-dimensional nanomaterials.

Automated summary

Despite the current challenges in commercial-scale graphene production using chemical vapor deposition (CVD), this study introduces a new approach combining high-throughput density functional theory and machine learning to identify new prospective catalyst materials with comparable performance to established catalysts. The approach discovered combinations of early- and late-transition metals, including unconventional partners like Zr, Hf, and Nb. This study highlights the importance of finding novel catalyst materials for CVD growth of low-dimensional nanomaterials.