(699d) Methods to Exploit Large Datasets in Catalysis

Computational catalysis techniques, such as density functional theory (DFT), have historically generated datasets of limited size due to the relatively high cost of performing DFT calculations. The cost of gathering DFT data has been decreasing because of the proliferation of public databases and advances in both computing power and workflow management. The decrease in computational cost has produced larger datasets, which offers new opportunities and challenges for data analysis. We present various methods that we use to analyze larger datasets to successfully gain new insights into theoretical catalysis.