Quantum computers have presented a new breakthrough that can solve the challenging problems of complex material design, which were difficult to resolve using traditional classical computing.
Professor Kim Ji-han and his research team from the Korea Advanced Institute of Science and Technology (KAIST) announced on the 9th that they have developed a new framework that efficiently explores the design space of millions of multicomponent porous materials (MTV) using quantum computers. The research results were published online on August 22 in the journal 'ACS Central Science.'
Multivariate porous materials (MTV) are materials that can be custom-designed at the molecular level, resembling a 'set of Lego blocks', allowing for the free implementation of desired structures. Utilizing this, it is possible to contribute significantly to solving environmental problems and advancing next-generation energy technologies, including energy storage and conversion.
However, as the variety of components increases, the number of possible combinations grows exponentially, making it impossible to design and predict the properties of complex linker combinations of MTV structures using the traditional method of checking each structure one by one with classical computers.
The research team expressed the complex porous structure as a 'network drawn on a map' and converted each connection point and block type into qubits that the quantum computer could handle. They tasked the quantum computer with solving the problem, 'What type and ratio of blocks will lead to the most stable structure?'
Quantum computers can calculate multiple scenarios simultaneously. It is like laying out millions of Lego houses at once and quickly selecting the sturdiest one among them. Thanks to this, it can explore a vast number of possibilities that traditional computers had to calculate one by one with far fewer resources.
The research team also experimented with four actual reported MTV structures, showing that the results matched both in simulations and on the IBM quantum computer, indicating that it works effectively in practice.
In the future, they plan to expand this method into a platform that considers not only simple structural design but also synthesis possibilities, gas adsorption performance, and electrochemical properties all at once, in conjunction with machine learning.
Professor Kim Ji-han stated, 'This study is the first case of resolving the bottleneck of complex multicomponent porous material design with quantum computing.' He noted that this achievement is expected to be widely applied in fields where precise composition is key, such as carbon capture and separation, selective catalytic reactions, and ion-conducting electrolytes, and can flexibly extend to more complex systems in the future.
References
ACS Central Science (2025), DOI: https://doi.org/10.1021/acscentsci.5c00918