Data in catalysis research is analyzed using both experimental and computational methods. Experimental techniques such as temperature-programmed desorption (TPD), temperature-programmed reduction (TPR), and mass spectrometry provide empirical data. Computational methods involve density functional theory (DFT) and molecular dynamics simulations to predict catalyst behavior and interpret experimental results. Machine learning is also increasingly being used to analyze large datasets and predict catalyst performance.
