Advancing 2D material predictions: superior work function estimation with atomistic line graph neural networks

Abstract

Despite the increased research and scholarly attention on two-dimensional (2D) materials, there is still a limited range of practical applications for these materials. This is because it is challenging to acquire properties that are usually obtained by experiments or first-principles predictions, which require substantial time and resources. Descriptor-based machine learning models frequently require further density functional theory (DFT) calculations to enhance prediction accuracy due to the intricate nature of the systems and the constraints of the descriptors employed. Unlike these models, research has demonstrated that graph neural networks (GNNs), which solely rely on the systems' coordinates for model description, greatly improve the ability to represent and simulate atomistic materials. Within this framework, we employed the Atomistic Line Graph Neural Network (ALIGNN) to predict the work function, a crucial material characteristic, for a diverse array of 2D materials sourced from the Computational 2D Materials Database (C2DB). We found that the ALIGNN algorithm shows superior performance compared to standard feature-based approaches. It attained a mean absolute error of 0.20 eV, whereas random forest models achieved 0.27 eV.