CAREER: Advancing Understanding of Adsorption, Transport, and Interpenetration in Metal-Organic Frameworks with Pore Graphs

Metal-organic frameworks (MOFs) are materials used in important industrial applications such as chemical separations, energy storage, and water harvesting. MOFs contain intricate networks of nano-sized pores. The geometry of the networks determines how molecules move through the MOF and interact with the networks’ pore walls. Computational analysis could help describe the molecular motion, but current methods are limited because they do not capture the complexity of the pore network and chemical reactions that take place inside it. This CAREER project will construct a computational scheme based on a pore graph to quantify complex pore networks and their chemistry. The pore graph will be combined with molecular modeling and machine learning to better understand molecular motions and dynamics in MOFs. The results will accelerate the design of next-generation MOF-based materials for more efficient chemical separations and energy technologies. The project will train students in artificial intelligence (AI) and machine learning (ML). A new chemical engineering course will increase AI awareness among students. Summer workshops and an online course in AI will be created for professional education. Summer camps and a partnership with a local high school will build AI literacy among pre-college students. This CAREER project will develop an integrated framework combining graph theory, molecular modeling, and ML to understand confined phenomena in MOFs where current methods fall short. The core innovation will be the development of the pore graph, a unified mathematical representation that transforms intricate pore networks and chemistry into quantifiable objects. This integrated framework will be applied to address unresolved scientific challenges in three key areas: (1) adsorption thermodynamics, to reveal why larger pores anomalously condense earlier or simultaneously with smaller ones, impacting experimental characterization for MOFs; (2) molecular transport, to elucidate how structural defects alter pore networks and chemistry in MOF membranes and thereby determine material’s performance degradation for H2/CH4 separation, bridging the gap between laboratory design and industrial deployment; and (3) confined material assembly, to enable scalable generation of interpenetrated MOFs, unlocking a new domain of stable nanoporous materials for computational exploration. A combination of interpretable ML models, topological analysis, and graph algorithms uniquely enabled by the pore graph will be developed to achieve the goal. The research efforts will be closely integrated with the educational goal to increase AI/ML literacy among chemical engineering students, engineering professionals, and high school students. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2543449 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Kaihang Shi | Institution: SUNY at Buffalo, AMHERST, NY | Award Amount: $549,292 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2543449 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2543449.html