Supplementary Data: Machine Learning Framework for Stability Evaluation of Dual-Atom Catalysts

ML-DACs is a machine learning framework for the evaluation of the stability of Dual-Atom Catalysts against aggregation. Using a machine learning approach, this framework automates the workflow from data preprocessing to model training and evaluation. It enables researchers to efficiently predict DAC stability, supporting the rapid screening and design of new catalyst materials. This supplementary data repository contains the stability descriptors and the DFT-simulated adsorption energies of DACs on N-doped carbon used within the ML-DACs framework. Files Included: ml_dacs_supplementary_data.csv : The dataset in CSV format. ml_dacs_supplementary_data.pkl : Pickle version of the same dataset. README.md: Description of the dataset and feature definitions.