A data-based new material development methodology has been experimentally proven, and it is expected that the combination of artificial intelligence (AI) and automation technology will enable faster and more effective new material development.

A research team led by Professor Won-Young Choi of the Department of Chemistry at UNIST has succeeded in synthesizing three new zeolite-mimicking MOFs (ZIFs) using a data-based structure prediction algorithm in joint research with the Korea Institute of Science and Technology (KIST).

This study suggests the possibility of overcoming existing limitations in ZIF development and significantly increasing the speed of new material development.

MOF (Metal-Organic Framework) is a material that combines metal and organic substances to form a nano-scale porous structure.

In particular, ZIF (Zeolitic Imidazolate Framework) resembles the structure of zeolite, has excellent chemical stability, and has high flexibility in pore design, so it plays an important role in catalysis, gas storage, and separation technology.

On the other hand, although millions of ZIFs are theoretically possible, only 50 types have actually been synthesized, and the gap between theory and reality has been an obstacle to the development of new materials.

Professor Choi's team developed a prediction algorithm that quantifies and applies the intuition of existing chemists.

This algorithm is used between atoms. After analyzing bond angles, atomic ring structure connectivity, and connection regularities, 4,450,797 virtual structures were narrowed down to 420 types, and 90 top candidates (Tier 1) were selected based on energy stability.

As a result of experiments with some of these, we succeeded in synthesizing three new ZIFs: UZIF-31, UZIF-32, and UZIF-33.

The three newly synthesized ZIFs were confirmed to be highly functional materials capable of selectively separating carbon dioxide and methane.

In particular, UZIF-33 is evaluated to have excellent potential for greenhouse gas separation and purification, as it exhibits the characteristic of selectively adsorbing carbon dioxide 10 times more than methane.

Professor Choi Won-young said, “This study shows that digital prediction can lead to experimental results,” and predicted, “If combined with automated synthesis technology, it will be possible to dramatically increase the speed of developing new materials with desired properties.”

The results of this study were published on March 24th and selected as the cover paper of the world-renowned chemistry journal JACS Au. The research was conducted with the support of the Ministry of Science and ICT, the Institute of Information and Communication Technology Planning and Evaluation (IITP), the National Research Foundation of Korea (NRF), the Korea Institute of Science and Technology (KIST), and the UNIST Carbon Neutral Fusion Research Project.