Speaker
Description
Within the expansive domain of Metal-Organic Frameworks (MOFs), navigating the vast datasets for impactful research has posed significant challenges. Addressing this, our study introduces a groundbreaking methodology through MOFGalaxyNet, employing Social Network Analysis (SNA) to illuminate the structure and dynamics of MOF interactions. The core of our strategy, the Black Hole approach, identifies the most influential MOFs—akin to celestial black holes for their significant pull on surrounding entities. This leads to creating the Black Hole dataset, a curated collection of MOFs identified for their pivotal roles within the network. Through sophisticated SNA, we extract the Black Hole dataset, a concise yet comprehensive assembly of influential MOFs poised for significant breakthroughs in research. The Black Hole dataset, derived from advanced community detection and centrality analysis, also provides a focused, high-value resource for ML applications in MOF research. Utilizing the Girvan-Newman algorithm, we segment MOFGalaxyNet into communities, employing Degree and Betweenness centrality measures to highlight key MOFs. The resultant Black Hole dataset not only streamlines research focus towards MOFs with the highest potential impact but also embodies the FAIR (Findable, Accessible, Interoperable, Reusable) principles, offering a robust foundation for ML-driven advancements in materials science. Applying the Girvan-Newman algorithm for community detection, alongside Degree and Betweenness centrality analyses, facilitates identifying and categorizing Black Hole MOFs within MOFGalaxyNet. This methodology empowers ML in MOF Research by providing ML practitioners in the MOF community with a data-rich, targeted, and technically vetted resource for predictive modeling and algorithm training.