Data for "Literature-data-driven explainable machine learning for applicability-domain-constrained virtual screening of Ru-based CO₂ methanation catalysts"
Description
This dataset supports the manuscript entitled “Literature-data-driven explainable machine learning for applicability-domain-constrained virtual screening of Ru-based CO₂ methanation catalysts”. The dataset contains 556 experimental entries for Ru-based CO₂ methanation collected and curated from 41 literature DOI sources. It includes catalyst composition and preparation parameters, Ru loading, support and promoter information, active-phase particle size, BET surface area, pretreatment conditions, reaction temperature, pressure, gas hourly space velocity (GHSV), H₂/CO₂ ratio, CO₂ conversion, and CH₄ selectivity. The data were used to develop machine-learning models for CO₂ conversion regression and high-CH₄-selectivity classification (CH₄ selectivity ≥ 95%), together with explainability analysis and applicability-domain-constrained virtual screening. Associated code and screening results are provided to facilitate reproducibility and further analysis. The dataset is derived from previously published literature and is intended for research and academic use.
Files
Steps to reproduce
Download all files provided in this repository. The main dataset (ru_final.xlsx) contains the curated literature-derived data used for machine-learning model development. Run Ru_ML.py in a Python environment after setting the appropriate input and output file paths. The workflow performs data preprocessing, CO₂ conversion regression, high-CH₄-selectivity classification, model evaluation, SHAP-based interpretation, applicability-domain assessment, and virtual candidate screening. The external_validation_dataset.xlsx file is provided for additional model validation. Top_10_Candidates.xlsx and Pareto_Candidates.xlsx contain the ranked virtual-screening results. Detailed descriptions of the files, variables, model targets, and workflow are provided in README.txt.
Institutions
- Foshan UniversityGuangdong, Foshan