Artificial Intelligence Meets Laboratory Automation in Discovery and Synthesis of Metal–Organic Frameworks: A Review

This review discusses the transformative impact of the convergence of artificial intelligence (AI) and laboratory automation on the discovery and synthesis of metal–organic frameworks (MOFs). MOFs, known for their tunable structures and extensive applications in fields such as energy storage, drug delivery, and environmental remediation, pose significant challenges due to their complex synthesis processes and high structural diversity. Laboratory automation has streamlined repetitive tasks, enabled high-throughput screening of reaction conditions, and accelerated the optimization of synthesis protocols. The integration of AI, particularly Transformers and large language models (LLMs), has further revolutionized MOF research by analyzing massive data sets, predicting material properties, and guiding experimental design. The emergence of self-driving laboratories (SDLs), where AI-driven decision-making is coupled with automated experimentation, represents the next frontier in MOF research. While challenges remain in fully realizing the potential of this synergistic approach, the integration of AI and laboratory automation heralds a new era of efficiency and innovation in the discovery and engineering of MOF materials.