Over the past two decades, advances in high-throughput computations, automated workflows, and artificial intelligence have transformed materials discovery for metal organic frameworks (MOFs). Expanding databases have enabled the screening of millions of structures and identified promising candidates for diverse applications. However, the field seems to approaching a “materials database plateau” where larger databases no longer yield proportional gains in scientific knowledge or experimentally realized materials. The key challenge is shifting from finding promising materials to identifying those that can be synthesized, shaped, scaled, and applied. This viewpoint discusses this transition from data-rich screening to intelligence-driven materials discovery, where extracting knowledge becomes more important than generating more data.
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Seda Keskın (2026) studied this question.
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