ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT
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ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
Revisiting the MIL-101 metal–organic framework: design, synthesis, modifications, advances, and recent applications - Journal of Materials Chemistry A (RSC Publishing) DOI:10.1039/D1TA06238G
ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning
Recent advances in computational modeling of MOFs: From molecular simulations to machine learning - ScienceDirect
Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications - Macreadie - 2023 - Angewandte Chemie International Edition - Wiley Online Library
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The top 10 most common a) organic; and b) inorganic substructures in
Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
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