Machine learning of borophene-boride hetero-structures for borophene synthesis

Borophene, two-dimensional (2D) boron, exhibits versatile properties which may lead to a variety of applications, such as CO₂ reduction, hydrogen and oxygen evolution reactions, and superconductivity. However, a big challenge in borophene field is synthesis. Though borophene has been synthesized on several metal substrates, its strong interaction with substrates limit the achievement of free-standing borophene. Recently, it was found that boride is formed between borophene and substrate during borophene synthesis on Al (111). Metal borides have the potential to be superior substrates, compared to metals, for borophene synthesis and separation. Here to search for good substrates for borophene synthesis, we firstly generate a dataset of approximately 100 borophene-boride hetero-structures by density functional theory (DFT), then perform machine learning (ML) to analyze the interaction in the hetero-structures. For ML, random forest algorithm is used. Furthermore, we use the ML model in a larger dataset of hypothetical borides to predict good substrates. This work allows us to explore alternative routes of borophene synthesis.