The Rise of Generative AI for Metal-Organic Framework Design and Synthesis
This work tackles the challenge of efficiently designing high-performance MOF materials for clean air and energy applications, representing a shift in paradigm rather than an incremental improvement.
The paper addresses the problem of laborious design and discovery of metal-organic frameworks (MOFs) by introducing generative AI approaches, resulting in a new paradigm for accelerated, closed-loop discovery pipelines that autonomously propose and synthesize novel porous structures for clean air and energy applications.
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.