How Is ML Integrated with Experimental and Computational Methods?
Machine learning is often integrated with experimental and computational methods to create a synergistic approach in catalysis research. For instance, ML models can predict the outcomes of density functional theory (DFT) calculations, which are computationally expensive. This allows for rapid screening of catalyst candidates before conducting detailed DFT studies. Experimental data can be continuously fed into ML models to improve their accuracy and reliability over time.
