The rise of generative AI for metal-organic framework design and synthesis
Year: 2026
Type: article
Abstract: Advances in generative artificial intelligence are transforming how metal-organic frameworks (MOFs) are designed and discovered. This perspective introduces the shift from laborious enumeration of MOF candidates to generative approaches that can autonomously propose and synthesize in the laboratory new porous reticular structures on demand. We outline the progress of employing deep learning models, such as variational autoencoders, diffusion models, and large language-model-based agents, that are fueled by the growing amount of available data from the MOF community and suggest novel crystalline materials designs. These generative tools can be combined with high-throughput computational screening and even automated experiments to form accelerated, closed-loop discovery pipelines. The result is a new paradigm for reticular chemistry in which AI algorithms more efficiently direct the search for high-performance MOF materials for clean air and energy applications. Finally, we highlight remaining challenges such as synthetic feasibility, dataset diversity, and the need for further integration of domain knowledge.
Source: Matter
Institutions: Cambridge Intellectual Property (United Kingdom), Innovative Technology and Science (United Kingdom), University of California, Los Angeles, Fujian Normal University, Washington University in St. Louis, Kansas State University, Massachusetts Institute of Technology, Cornell University, University of Toronto, Vector Institute, Sungkyunkwan University, University of Washington, University of North Texas, École Polytechnique Fédérale de Lausanne
Language: English
Subfield: Inorganic Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $9,350
Funders: National Science Foundation, Canadian Institute for Advanced Research, University of Washington, Schmidt Family Foundation, EQT Foundation, Royal Society of Chemistry, University of Toronto, Canada First Research Excellence Fund, Molecular Engineering Materials Center, University of Washington, National Research Council Canada, Natural Resources Canada, Division of Materials Research, Eric and Wendy Schmidt
OpenAlex ID: W4414990951