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

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.