This study presents a data-efficient foundation model for porous materials, developed using expert-guided supervised learning. The approach aims to address the challenge of data scarcity in material artificial intelligence. The researchers constructed a supervised learning pipeline that incorporates expert knowledge to enhance the model's performance, potentially overcoming the limitations of traditional data-driven approaches.