This research introduces AceWGS, an innovative framework that combines large language models with traditional AI to accelerate the discovery of catalysts for Water-Gas Shift (WGS) reactions in hydrogen production.

• Integrates text-based chemical knowledge with numerical data to enhance catalyst design
• Develops a multi-modal framework that processes both structured and unstructured data
• Demonstrates significant improvements in catalyst discovery efficiency for low-temperature WGS reactions
• Provides practical pathways to optimize hydrogen production for fuel cells

This engineering breakthrough matters because it addresses a critical bottleneck in hydrogen energy production, potentially accelerating the transition to cleaner energy sources while reducing development costs and timelines.

AceWGS: An LLM-Aided Framework to Accelerate Catalyst Design for Water-Gas Shift Reactions