With the help of large language models, deep neural network learning, and kinetic calculation, a knowledge graph prediction of prior research in methane conversion is conducted. This knowledge graph provides a comprehensive understanding of the relationships among catalyst materials, catalytic performance, and reaction conditions, providing guidance for future experimental tasks to accelerate industrial catalyst development.
Abstract
Selective conversion of methane to carbon-based compounds is promising but currently limited by issues related to scale. Unraveling the interconnected relationships between products selectivity and methane conversion plays a pivotal role in understanding of this complex process. In this work, a knowledge graph (KG) is constructed for methane conversion with the help of a robust large language model basing on the literature reported methane conversion over different catalysts under various conditions. This KG, structured around 11 entity types and 32 relationship types, allows to effectively analyze advancements in methane conversion – including identifying optimal catalytic processes, evaluating reaction conditions, and tracking development trends. A deep neural network analysis of the KG highlighted catalysts with metal active sites and multifunctional supports as particularly effective for methanol production under conditions suitable for industrial-scale applications. These findings provide valuable insights for targeted catalyst development and industrial applications.
Advanced Science, EarlyView. Read More
