ArticleACS applied materials & interfaces2021
Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening.
Article in ACS applied materials & interfaces, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Intelligent Computing for Future Layered Cathodes in Sodium-Ion Batteries.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- A Visualization Analysis of Machine Learning Applications in Gas Adsorption Using Nanoporous Materials.Nanomaterials (Basel, Switzerland) · 2026Article
- Developments in nitrous oxide capture technologies: bridging current research to clinical applications.Anaesthesia · 2026Review
- Fragment to framework: automatic fragmentation of covalent organic frameworks into building blocks for band gap analysis.Materials chemistry frontiers · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- Discovering metal-organic framework/polymer mixed-matrix membranes via machine learning for COCommunications materials · 2026Article
- Computational Simulations of Metal-Organic Frameworks to Enhance Adsorption Applications.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Symmetry is the Key to the Design of Reticular Frameworks.Advanced materials (Deerfield Beach, Fla.) · 2025Article
- Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CHThe journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Exploring the chemical design space of metal-organic frameworks for photocatalysis.Chemical science · 2025Article
- MOFChecker: a package for validating and correcting metal-organic framework (MOF) structures.Digital discovery · 2025Article
- Machine Learning Descriptors for COMolecules (Basel, Switzerland) · 2025Review
- Hydrostable Fluorinated Metal-Organic Frameworks for COChem & bio engineering · 2024Article
- Deep learning-based recommendation system for metal-organic frameworks (MOFs).Digital discovery · 2024Article
- The Open DAC 2023 Dataset and Challenges for Sorbent Discovery in Direct Air Capture.ACS central science · 2024Article
- Accelerated Discovery of Metal-Organic Frameworks for COIndustrial & engineering chemistry research · 2024Review
- Identifying pathways to metal-organic framework collapse during solvent activation with molecular simulations.Journal of materials chemistry. A · 2023Article
- MOFGalaxyNet: a social network analysis for predicting guest accessibility in metal-organic frameworks utilizing graph convolutional networks.Journal of cheminformatics · 2023Article
- A Robust Framework for Generating Adsorption Isotherms to Screen Materials for Carbon Capture.Industrial & engineering chemistry research · 2023Article
- An Ecosystem for Digital Reticular Chemistry.ACS central science · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
By combining metal nodes and organic linkers, an infinite number of metal organic frameworks (MOFs) can be designed in silico. Therefore, when making new databases of such hypothetical MOFs, we need to ensure that they not only contribute toward the growth of the count of structures but also add different chemistries to the existing databases. In this study, we designed a database of ∼20,000 hypothetical MOFs, which are diverse in terms of their chemical design space─metal nodes, organic linkers, functional groups, and pore geometries. Using machine learning techniques, we visualized and quantified the diversity of these structures. We find that on adding the structures of our database, the overall diversity metrics of hypothetical databases improve, especially in terms of the chemistry of metal nodes. We then assessed the usefulness of diverse structures by evaluating their performance, using grand-canonical Monte Carlo simulations, in two important environmental applications─post-combustion carbon capture and hydrogen storage. We find that many of these structures perform better than widely used benchmark materials such as Zeolite-13X (for post-combustion carbon capture) and MOF-5 (for hydrogen storage). All the structures developed in this study, and their properties, are provided on the Materials Cloud to encourage further use of these materials for other applications.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.