Supporting data for "Emerging Computational Approaches For Predictive Nanomaterial Design And Redox Mechanism Exploration Toward Efficient Electrocatalysis"

Computational techniques play a critical role in the design and optimization of electrocatalysts, offering invaluable insights into their structure-function relationships and catalytic mechanisms for various electrocatalytic reactions. By employing density functional theory (DFT) calculation, researchers can analyze the electronic structure, explore the reaction mechanism, predict material properties, and identify promising catalyst candidates with enhanced activity, selectivity, and stability. The main idea of this thesis is utilizing emerging computational approaches to explore redox mechanism and predict nanomaterials design toward efficient electrocatalysis.

Here, in this dataset, it contains the nanomaterials structure optimized by DFT calculations. The DFT calculated energy for each structure. Then training data and the testing data of machine learning model were included in this dataset. Finally, the constructed Wulff constructions, phase diagrams and free energy diagrams were all included in this dataset.