ReviewJournal of chemical information and modeling2021
Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation.
Review in Journal of chemical information and modeling, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
32 citing papers in PubMed, 222 citations in OpenAlex.
- GFN1-xTB-Assisted Machine Learning for Electronic Structure Screening of Metal-Organic Frameworks.Journal of chemical theory and computation · 2026Article
- Biomacromolecule-MOF Composites for Intracellular Delivery: From Empirical Construction to Rational Design and AI-Assisted Screening.Small (Weinheim an der Bergstrasse, Germany) · 2026Review
- A Visualization Analysis of Machine Learning Applications in Gas Adsorption Using Nanoporous Materials.Nanomaterials (Basel, Switzerland) · 2026Article
- Molecular Simulation of Hydrogen Systems: From Properties and Methods to Applications and Future Directions.Chemical reviews · 2025Review
- Advancing Metal-Organic Framework-Based Composites for Effective Chemical Warfare Agent Detoxification under Real-World Conditions.Advanced materials (Deerfield Beach, Fla.) · 2025Review
- Adsorption Mechanisms and AI-Driven Discovery of Biomass-Based COSmall (Weinheim an der Bergstrasse, Germany) · 2025Review
- MOFGPT: Generative Design of Metal-Organic Frameworks using Language Models.Journal of chemical information and modeling · 2025Article
- Artificial Intelligence Paradigms for Next-Generation Metal-Organic Framework Research.Journal of the American Chemical Society · 2025Review
- Application of Metal-Organic Frameworks Nanoparticles in the Diagnosis and Treatment of Breast Cancer.International journal of nanomedicine · 2025Review
- Self-Driving Laboratories for Chemistry and Materials Science.Chemical reviews · 2024Review
- Design and Synthesis of Red-Absorbing Fluoran Leuco Dyes Supported by Computational Screening.ACS omega · 2024Article
- Deep learning-based recommendation system for metal-organic frameworks (MOFs).Digital discovery · 2024Article
- Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology.Chemistry of materials : a publication of the American Chemical Society · 2024Article
- ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models.Nature communications · 2024Article
- A comprehensive transformer-based approach for high-accuracy gas adsorption predictions in metal-organic frameworks.Nature communications · 2024Article
- A generative artificial intelligence framework based on a molecular diffusion model for the design of metal-organic frameworks for carbon capture.Communications chemistry · 2024Article
- Advances and Applications of Metal-Organic Frameworks (MOFs) in Emerging Technologies: A Comprehensive Review.Global challenges (Hoboken, NJ) · 2024Review
- Machine Learning-Assisted Low-Dimensional Electrocatalysts Design for Hydrogen Evolution Reaction.Nano-micro letters · 2023Review
- A reliable QSPR model for predicting drug release rate from metal-organic frameworks: a simple and robust drug delivery approach.RSC advances · 2023Article
- Advancing CHJournal of materials chemistry. A · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The acceleration in design of new metal organic frameworks (MOFs) has led scientists to focus on high-throughput computational screening (HTCS) methods to quickly assess the promises of these fascinating materials in various applications. HTCS studies provide a massive amount of structural property and performance data for MOFs, which need to be further analyzed. Recent implementation of machine learning (ML), which is another growing field in research, to HTCS of MOFs has been very fruitful not only for revealing the hidden structure-performance relationships of materials but also for understanding their performance trends in different applications, specifically for gas storage and separation. In this review, we highlight the current state of the art in ML-assisted computational screening of MOFs for gas storage and separation and address both the opportunities and challenges that are emerging in this new field by emphasizing how merging of ML and MOF simulations can be useful.
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What OpenQuestion holds
Registered trials
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.