Achieving interpretability involves several techniques and approaches: - Experimental Techniques: Methods such as spectroscopy, microscopy, and kinetic studies provide insights into the structure and behavior of catalysts. - Computational Modeling: Density Functional Theory (DFT) and molecular dynamics simulations help in understanding the electronic structure and reaction pathways. - Machine Learning: Data-driven approaches can predict catalyst performance and identify important features contributing to activity and selectivity.
