Insider Brief
- KAIST researchers used quantum computing to design multicomponent porous materials (MTVs), marking the first time quantum devices solved this class of materials problem.
- The method enables efficient exploration of millions of possible molecular structures, reducing the computational limits faced by classical approaches.
- Experimental validation confirmed the reliability of the quantum-designed MTVs, with potential applications in energy storage, catalysis, and carbon capture.
- Image: From left: Professor Jihan Kim of the Department of Biochemical Engineering, Shinyoung Kang PhD Program, Younghoon Kim PhD Program
South Korean scientists have turned to quantum computing to solve a bottleneck in designing multicomponent porous materials, opening a path toward more efficient energy storage, carbon capture and catalytic technologies, according to a news release.
Researchers at Korea Advanced Institute of Science and Technology (KAIST) said their work marks the first use of quantum computers to create multivariate porous materials, often called MTVs, which can be customized at the molecular level. The team’s findings, published in ACS Central Science, show that quantum devices can map and evaluate millions of possible structures far faster than conventional methods.
Building Blocks at the Molecular Scale
MTVs are created by linking organic molecules and metal clusters into a porous framework. Because the structure can be precisely tailored, these materials are often compared to Lego sets, where different combinations can be arranged to yield new properties. Such versatility makes MTVs promising for gas separation, chemical sensors, catalysis and next-generation batteries.
But complexity has limited progress. As the number of potential building blocks increases, the number of combinations expands exponentially, making it impractical for classical computers to explore all possibilities. KAIST researchers said this complexity has been the main obstacle in developing MTVs for real-world applications.
To overcome the challenge, the KAIST team led by Professor Jihan Kim represented the porous frameworks as networks of nodes and links. Each element of the structure was then encoded as qubits on a quantum computer. The problem was reframed as determining which combinations of building blocks would yield the most stable material, a task well-suited to quantum devices that can evaluate many outcomes simultaneously.
According to KAIST, the approach effectively allowed the computer to sift through millions of possible frameworks at once, rather than one by one. The result was a dramatic reduction in the computational resources needed to identify viable materials.
From Simulation to Experiment
The research team also validated its simulations by synthesizing four MTV structures predicted by the quantum computer. The KAIST team reported that the experimental results matched the simulations, demonstrating the method’s reliability and underscoring its potential for practical use.
The study further suggests that combining quantum computing with machine learning could accelerate MTV design even more. The researchers added that they plan to build a platform that not only identifies stable structures but also predicts synthesis pathways, gas absorption properties and electrochemical behavior.
Implications for Energy and the Environment
The ability to design MTVs efficiently has broad implications. the news release indicated that precision control over composition could make it possible to develop catalysts for selective reactions, electrolytes for high-performance batteries, and materials for separating greenhouse gases. The method could also be applied to even more complex systems in the future, widening the scope of materials science research.
The work was carried out by Kim’s team in the Department of Biochemical Engineering, with contributions from Kang Shin-young and Dr. Younghoon Kim. KAIST emphasized that this study represents a milestone where quantum computing directly solved a materials design problem rather than serving as a theoretical exercise.
As quantum hardware improves, the university said its approach may scale to increasingly intricate materials problems, positioning quantum computing as a practical tool for tackling climate and energy challenges.