DOE OSTI · 3370420
Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence
Abstract
The traditional development of novel metal–organic frameworks (MOFs) is often hindered by challenges such as synthetic accessibility and time- and resource-intensive experimentation. High-throughput, automated experimental and computational techniques have enabled rapid chemical space exploration and theoretical MOF design. When combined with artificial intelligence (AI), these methods can be used to lead autonomous laboratories to new frontiers for MOF discovery, where these materials can be designed for a specific application, efficiently synthesized, characterized, and evaluated. Here, this perspective highlights the role of AI in advancing automated MOF synthesis and characterization, computational MOF design and screening, and the integration of these approaches within autonomous workflows to ultimately enable the MOF laboratories of the future.
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Gaidimas, Madeleine A. [Northwestern University, Evanston, IL (United States)], Bai, Jiaru [University of Toronto, ON (Canada); Vector Institute for Artificial Intelligence, Toronto, ON (Canada)], Kang, Yeonghun [University of Toronto, ON (Canada); Vector Institute for Artificial Intelligence, Toronto, ON (Canada)], Kirlikovali, Kent O. [Northwestern University, Evanston, IL (United States)], Bernales, Varinia [University of Toronto, ON (Canada); Acceleration Consortium, Toronto, ON (Canada)], Aspuru-Guzik, Alán [University of Toronto, ON (Canada); Vector Institute for Artificial Intelligence, Toronto, ON (Canada); Acceleration Consortium, Toronto, ON (Canada); Canadian Institute for Advanced Research (CIFAR), Toronto, ON (Canada); Nvidia, Toronto, ON (Canada)], Farha, Omar K. [Northwestern University, Evanston, IL (United States)] (ORCID:0000000299049845). 2026-02-25. Reimagining metal-organic framework discovery: Integrating experiment, computation, and artificial intelligence. https://doi.org/10.1016/j.chempr.2025.102921
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