ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT
Descrição
Integrating stability metrics with high-throughput computational screening of metal–organic frameworks for CO2 capture
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
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
Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications - Macreadie - 2023 - Angewandte Chemie International Edition - Wiley Online Library
Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications - Macreadie - 2023 - Angewandte Chemie International Edition - Wiley Online Library
Expanding Linker Dimensionality in Metal‐organic Frameworks for sub‐Ångstrom Pore Control for Separation Applications - Macreadie - 2023 - Angewandte Chemie International Edition - Wiley Online Library
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
PDF) Inverse design of metal-organic frameworks for direct air capture of CO2 via deep reinforcement learning
Molecules, Free Full-Text
A database of ultrastable MOFs reassembled from stable fragments with machine learning models - ScienceDirect
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
Accelerating Discovery of Metal–Organic Frameworks for Methane Adsorption with Hierarchical Screening and Deep Learning
Recent advances in computational modeling of MOFs: From molecular simulations to machine learning - ScienceDirect
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