ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning

ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning

Flow diagram for the construction of CoRE MOF 2019 database

A database of ultrastable MOFs reassembled from stable fragments with machine learning models - ScienceDirect

Nanoengineering Metal–Organic Frameworks and Derivatives for Electrosynthesis of Ammonia

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

Towards modeling spatiotemporal processes in metal–organic frameworks: Trends in Chemistry

J. Compos. Sci., Free Full-Text

Nanoengineering Metal–Organic Frameworks and Derivatives for Electrosynthesis of Ammonia

Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening

Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications - Macreadie - 2023 - Angewandte Chemie International Edition - Wiley Online Library