How is Data Collected for Machine Learning in Catalysis?
Data collection is a critical step for any machine learning project. In catalysis, data can be collected from various sources:
Experimental Data: Results from laboratory experiments provide valuable information about catalyst performance. Computational Simulations: Computational chemistry techniques, such as Density Functional Theory (DFT), can simulate reactions and generate data. Literature: Published research papers and patents contain a wealth of information that can be used to train ML models.
