Data-Driven Strategies for Designing Multicomponent Molten Catalysts to Accelerate the Industrialization of Methane Pyrolysis

Methane (CH4) pyrolysis via molten media catalysis presents a transformative coke-free pathway for carbon-neutral hydrogen production and high-value carbon capture, yet its industrial scalability remains constrained by energy-intensive ultrahigh temperature requirements. Recent breakthroughs in multicomponent molten systems (i.e., binary, ternary, and quaternary) have shown great potential for effective CH4 pyrolysis at a moderate temperature, opening up exciting possibilities for the industrialization of CH4 pyrolysis. However, the combinatorial complexity of element selection and atomic disorder in molten media, coupled with the limitations of empirical experimentation and conventional computational methods, present great hurdles for multicomponent molten catalysts design. With the development of artificial intelligence and data technology, the data-driven strategy offers a promising alternative to the traditional experiment/theory approach. From this perspective, we expound and emphasize the data-driven approach for enhancing molten media catalysts discovery and advancing the industrialization development of CH4 pyrolysis. We discuss the capacity of this approach to explore the design space along with several data-guided strategies tackling the existing challenges associated with molten catalysts design.