Machine learning-ready datasets for Metal-Organic Framework materials research
Our datasets integrate multiple trusted data sources and are specifically designed for machine learning applications, providing structured data formats and comprehensive features for training predictive models and conducting large-scale screening.
A comprehensive dataset of MOFs for hydrogen storage, containing structural data and four key hydrogen adsorption capacity metrics derived from GCMC simulations at standard and temperature–pressure swing conditions.
A massive hypothetical MOF database with comprehensive structural and property data, enabling high-throughput computational screening, material discovery, and gas adsorption prediction for various applications.