Data-Driven Discovery of Reticular Framework Materials for Sustainable Future

Abstract

The demand for high-performance framework materials to address the ongoing global and energy challenges is significant. To expedite the discovery of these innovative framework materials, we must adopt new approaches in design, synthesis, and characterization practices.1-7 In this context, data science is poised to play a crucial role in propelling materials discovery towards sustainable future. Among the notable advancements, Metal-Organic Frameworks (MOFs) stand out as a new class of promising transformative materials, targeting applications in carbon neutrality. Despite the fact that MOFs offer virtually limitless combinations of metal nodes and organic building blocks, optimizing these materials to attain peak performance is a sophisticated endeavor, challenging both computational and synthetic chemists.

In light of these serious challenges, there is a growing trend toward digitizing chemical insights.1 The advanced digital archives could greatly accelerate the process of identifying groundbreaking future materials. In this presentation, we show detailed case studies, particularly emphasizing Zeolitic Imidazolate Frameworks (ZIFs), Zirconium-based MOFs and Metal-Organic Polyhedra (MOPs). Leveraging comprehensive databases—actively utilizing both hypothetical and experimentally confirmed entries—we elucidate their pivotal role in identifying synthetic targets, and further align with specific application goals for sustainable future.