The conversion of carbon dioxide into clean fuels is regarded as an important route toward carbon neutrality. CO 2 methanation, in particular, has drawn increasing interest due to its favorable thermodynamic properties and environmental benefits. Yet, large-scale deployment continues to face challenges such as insufficient catalyst activity at low temperatures and vulnerability to carbon deposition.
Researchers have now applied an explainable machine learning (ML) framework to support the rational design of nickel-based catalysts for CO 2 methanation. Instead of relying on traditional trial-and-error methods, the study introduces a systematic approach for data processing, cross-validation, and ensemble learning model construction. Among the tested methods, a categorical boosting (CatBoost) model achieved R² values of 0.77 for CO 2 conversion and 0.75 for CH 4 selectivity.
By analyzing key descriptors, the study identified optimal reaction conditions: temperature between 250-350 °C, gas hourly space velocity below 15,000 cm³ g⁻¹ h⁻¹, BET surface area of 50-200 m² g⁻¹, and nickel content higher than 5%. These insights demonstrate how data-driven methods can guide catalyst optimization and shorten the pathway from laboratory research to industrial application.
"This work shows how machine learning can help us better understand the critical factors influencing CO 2 methanation performance," said Hao Li, a Distinguished Professor at Tohoku University's Advanced Institute for Materials Research (WPI-AIMR. "By making the models explainable, we are not only predicting results but also gaining knowledge about why certain conditions matter."
Looking ahead, the research team will integrate density functional theory calculations and high-throughput experimental data to build multi-scale predictive models. They will also conduct systematic experimental validation to refine catalyst designs.
"Our goal is to establish a platform that combines computational chemistry, machine learning, and catalytic engineering," Li explained. "In doing so, we hope to contribute practical solutions for carbon recycling and the efficient use of renewable energy." This study provides a perspective on how explainable machine learning can be applied to catalyst research, supporting both the development of cleaner fuels and the broader transition to sustainable energy systems.
The study was published in the journal ACS Sustainable Chemistry & Engineering on August 22, 2025.
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.
ACS Sustainable Chemistry & Engineering
Application of an Explainable Machine Learning to CO2 Methanation for Optimal Design Nickel-Based Catalysts
22-Aug-2025