Core Viewpoint - The research team at Zhejiang University has developed an "AI-driven efficient low-temperature iron-based ammonia synthesis catalyst" project, introducing a triadic driving model of "data + intelligence + automation" to accelerate the development of ammonia synthesis industrial catalysts [1][2] Group 1: Project Overview - The project aims to address the long-standing industry pain points of high costs and lengthy cycles in catalyst development, which traditionally relies on trial and error [1] - The AI catalyst scientist laboratory platform integrates automated preparation, high-throughput evaluation, and machine learning modules, enabling intelligent full-process management from design to screening of catalysts [1] Group 2: Technological Advancements - The platform allows for the preparation of 500 different catalyst samples within 24 hours, a significant improvement compared to the traditional method that requires over 8 hours for a single catalyst [1] - The algorithm used in the platform can accurately design formulations without extensive repetitive experiments, covering over 100 million formulation optimization spaces and significantly reducing trial and error costs [1] Group 3: Future Applications - The team plans to launch the intelligent preparation and evaluation device for ammonia synthesis catalysts, aiming to create a fully automated catalyst development system centered on artificial intelligence [2] - The project will contribute to the green transformation and upgrading of China's ammonia synthesis industry and provide key technological support for renewable energy storage [2]