AI‐Powered Experimental Discovery of Metal‐Organic Frameworks for n/i‐Butane Separation

Abstract

Abstract There are significant challenges in developing efficient adsorbents as alternatives to the energy‐intensive distillation processes for n/i‐butane separation. Metal‐organic frameworks (MOFs) hold great potential in addressing this issue. However, the vast diversity of MOFs makes the discovery of high‐performance materials akin to searching for a needle in a haystack. Here, the high‐throughput screening based on artificial intelligence (AI) is employed to accelerate the identification of MOFs for n/i‐butane separation. An integrated descriptor system, accessible via both experiments and simulations, is proposed and broadly validated, demonstrating better performance over those widely‐used descriptors. In addition, an optimization strategy for training dataset is proposed based on similarity, allowing for the efficient model training with only 10% samples from the entire database and thus significantly reducing the costs. Leveraging the integrated descriptors and optimization strategy, MOFs with exceptional n/i‐butane separation performance are successfully identified through neural network model. As a proof of concept, SIFSIX‐3‐Zn is synthesized for validation because it has the largest n‐butane capacity among top 20 MOFs. The SIFSIX‐3‐Zn demonstrates outstanding n/i‐butane separation performance with nearly zero uptake of i‐butane. This work introduces a novel research paradigm integrating AI, simulation and experiment, and presents an efficient process with broad applicability for material discovery.