Data-driven strategies to advance methane pyrolysis catalysts

Methane (CH4) pyrolysis, a reaction that produces hydrogen without emitting carbon dioxide, often utilizes molten media catalysts. A recent research paper has explored how artificial intelligence and machine learning are helping scientists identify these catalysts more efficiently.

Details were published in the journal ACS Catalysis on June 11, 2025.

The industrial implementation of methane pyrolysis via molten media catalysis has been stymied by the need for extremely high temperatures, raising energy costs and technical barriers.

Recent breakthroughs have shown that multicomponent molten systems, including binary, ternary, and quaternary mixtures, can enable CH4 pyrolysis at more moderate temperatures. Yet, designing such systems is challenging due to the vast number of elemental combinations and the inherent atomic disorder within molten catalysts.

"To systematically explore the design space of multicomponent molten catalysts, data-driven approaches guided by AI offer significant advantages over traditional trial-and-error experimentation," says Hao Li, a professor from Tohoku University's Advanced Institute for Materials Research (WPI-AIMR) who led the study. "We propose descriptor-guided, generative model-guided, and active learning-guided strategies to accelerate catalyst discovery."

The research highlights how descriptor-guided design can identify key physicochemical properties influencing catalyst performance, while generative models can suggest novel compositions for experimental validation. Active learning frameworks, integrating continuous feedback from experiments, can further refine predictions to target optimal catalyst candidates.

"Our next steps involve developing machine learning-based molten catalyst models using high-quality training data," Li explains. "We also aim to build self-driven data acquisition platforms and advance multiscale simulation methods to better understand real reaction environments."

The team emphasizes the importance of large-scale experimental databases to support data-driven catalyst design. The Digital Catalysis Platform (DigCat), developed by the Hao Li Lab, is cited as the largest catalysis database and the first digital platform of its kind globally.

Li concludes, "By combining AI, machine learning, and experimental data, we hope to overcome the design bottlenecks of molten media catalysts and accelerate the industrialization of methane pyrolysis technologies."

About the World Premier International Research Center Initiative (WPI)

The WPI program was launched in 2007 by Japan's Ministry of Education, Culture, Sports, Science and Technology (MEXT) to foster globally visible research centers boasting the highest standards and outstanding research environments. Numbering more than a dozen and operating at institutions throughout the country, these centers are given a high degree of autonomy, allowing them to engage in innovative modes of management and research. The program is administered by the Japan Society for the Promotion of Science (JSPS).

See the latest research news from the centers at the WPI News Portal: https://www.eurekalert.org/newsportal/WPI
Main WPI program site:  www.jsps.go.jp/english/e-toplevel

Advanced Institute for Materials Research (AIMR)
Tohoku University
Establishing a World-Leading Research Center for Materials Science
AIMR aims to contribute to society through its actions as a world-leading research center for materials science and push the boundaries of research frontiers. To this end, the institute gathers excellent researchers in the fields of physics, chemistry, materials science, engineering, and mathematics and provides a world-class research environment.