DigCat's extensive data analysis also provides strong support for standardizing experiments and ensuring data reliability. By comparing the performance of common benchmark samples across multiple studies, users can easily identify outliers in the data and investigate potential causes. This helps streamline experimental procedures and data processing, ensuring more reliable and reproducible results. terials Database. By utilizing large language models to match the fundamental structures and properties of these materials with the demands found in literature, the AI Catalysis Scientist significantly broadens the potential materials range for catalyst prediction.