Research News – DigMethpy: An AI-driven platform to accelerate discovery of methane pyrolysis catalysts

Researchers have developed a new artificial intelligence-powered platform that could significantly speed up the discovery of methane pyrolysis catalysts, a promising technology to produce hydrogen with lower carbon emissions.

Hydrogen is widely considered a key component of future clean energy systems. However, many current hydrogen production methods produce carbon dioxide as a byproduct. Methane pyrolysis provides an alternative approach to splitting methane into hydrogen and solid carbon, avoiding direct carbon dioxide emissions.

One of the major challenges facing methane pyrolysis is identifying efficient molten catalysts that can accelerate the reaction. Because molten catalysts exist in a vast and poorly understood chemical design space, discovering effective materials has traditionally required extensive trial-and-error experiments.

To address this issue, an international research team developed DigMethpy, an AI-powered digital catalysis platform that combines scientific literature, experimental data, computational simulations, machine learning models, and large-scale language models into a single discovery framework.

The platform creates a closed-loop workflow that continuously collects information, predicts promising catalyst candidates, and improves recommendations through validation feedback. DigMethpy currently contains over 40,000 curated data points collected from over 500 scientific publications and computational records covering molten metals, alloys, salts, and mixed catalyst systems.

The researchers used this platform to identify important chemical properties related to catalyst performance, including atomic charge-related descriptors, diffusion behavior, and hydrogen adsorption properties. These insights were used to guide the design of highly active multicomponent molten alloy catalysts for methane pyrolysis.

The researchers believe this approach will allow scientists to make better use of growing amounts of scientific data while reducing the time and cost needed to discover new catalytic materials. The framework also shows how artificial intelligence can be integrated into materials research to support more efficient scientific decision-making.

“DigMethpy represents an important step toward data-driven and ultimately autonomous catalyst discovery,” said Hao Li, Distinguished Professor at Tohoku University Institute for Advanced Materials Research (WPI-AIMR). “By connecting experimental knowledge, computational modeling, machine learning, and large-scale language models in a unified workflow, we can accelerate the development of catalysts needed for cleaner hydrogen production and other sustainable energy technologies.”

The study was published in the journal AI Agents. Hao Li also serves as the founding editor of this journal. The research team plans to further expand the DigMethpy database, improve predictive capabilities, and develop a more autonomous multi-agent system that can support next-generation catalyst discovery.

Publication details:

title: DigMethpy: An AI-powered digital catalyst platform for methane pyrolysis melt catalyst design

author: Zihao Cheng, Xxuan Huan, Hangwei Liu, Junmei Du, Piao Ma, Hang ying, Di Zhang, Hao Li, Yuanzheng Chen

journal: AI agent

Doi: 10.20517/aiagent.2026.11