Researchers are exploring the use of machine learning models, including transformers and graph neural networks, to predict adsorption energy and mechanisms in various materials and interfaces. These models are being developed to analyze and understand the behavior of molecules on metal and porous material surfaces. The approaches being investigated include data-efficient foundation models, expert-guided supervised learning, and interpretable deep learning methods.