GraphReasoning is a project that utilizes generative AI techniques to transform 1,000 scientific papers into knowledge graphs. Through structured analysis, it computes node degrees, identifies communities and connectivity, evaluates clustering coefficients and betweenness centrality of key nodes, revealing a fascinating knowledge architecture. The graph exhibits scale-free properties and a high degree of interconnectivity, facilitating graph reasoning that leverages transitivity and isomorphism to uncover unprecedented interdisciplinary relationships for answering questions, identifying knowledge gaps, proposing innovative material designs, and predicting material behaviors.