[LAB Exploration] KAIST Develops Next-Generation Energy Storage Materials Using Quantum Computers

A technology has been developed that uses quantum computers to identify optimal new materials for energy storage, such as carbon capture.

Research team led by Professor Kim Jihan from the Department of Biological and Chemical Engineering at the Korea Advanced Institute of Science and Technology (KAIST). From left: Professor Kim, PhD candidate Kang Shinyoung, PhD candidate Kim Younghoon. Korea Advanced Institute of Science and Technology (KAIST)

On October 10, the Korea Advanced Institute of Science and Technology (KAIST) announced that the research team led by Professor Kim Jihan from the Department of Biological and Chemical Engineering has developed a technique for accurately designing the structure of multicomponent porous materials (MTVs) using quantum computers.

Porous materials are substances that can be custom-designed at the molecular level by combining various building blocks, such as organic ligands resembling connecting rods and metal clusters, allowing for the free realization of desired structures. With a wide range of possible combinations, these materials can trap specific molecules like carbon dioxide or facilitate chemical reactions, making them strong candidates for next-generation energy storage materials, including applications in catalysis, gas adsorption, and separation.

Until now, designing porous materials with diverse components has been difficult. As the variety of components increases, the number of possible combinations grows exponentially, making it impossible for conventional computers to calculate all combinations in a short period of time.

The research team addressed this challenge by leveraging quantum computers, which can calculate multiple scenarios simultaneously. They represented complex porous structures as graphs, converted each connection point and block type into qubits that a quantum computer can process, and designed the quantum computer to simultaneously calculate millions of possible arrangements for how and in what proportions different molecular blocks should be placed.

As a result, they succeeded in searching for material structures suitable for specific purposes more quickly and accurately than before. In fact, the team conducted experiments on four types of porous materials and demonstrated the same results through both simulation and IBM's quantum computer, thereby verifying the reliability of the technology.

This technology is expected to evolve into a platform that, when combined with machine learning, can simultaneously predict not only the structure of materials but also their synthesizability and performance.

This study was co-authored by doctoral candidates Kang Shinyoung and Kim Younghoon as joint first authors. The research findings were published online in the international journal ACS Central Science, an American Chemical Society journal, on August 22.

Professor Kim stated, "This research is the first case of overcoming the bottleneck in complex new material design using quantum computing," adding, "It will see widespread application in fields where precise material design is crucial, such as carbon capture and separation, selective catalytic reactions, and high-performance ion-conducting electrolytes."

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