AI-Assisted Design and Automated Synthesis of Atomic-Level Catalysts

Key Points

• The aim is to review the latest advancements in AI-assisted design and automated synthesis of atomic-level catalysts.
• Overview of machine learning models and graph neural networks for active site screening and structure-performance relationships.
• Discussion on the application of large language models in data extraction from catalyst literature.
• Summarization of advancements in automated synthesis and self-driven experimental platforms.
• AI methods reduce candidate space for atomic-level catalysts but face limitations in predicting synthesis feasibility.
• Large language models show potential in integrating cross-domain information but struggle with consistency and evaluation standards.
• Automated synthesis technologies facilitate efficient experimental data generation for catalyst development.