ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2023
Interpretable Machine-Learning and Big Data Mining to Predict Gas Diffusivity in Metal-Organic Frameworks.
Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Radiation-Resistant Kr-Selective MOF With Record Kr/Xe Selectivity at Elevated Pressure.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Flexible Metal-Organic Frameworks for Gas Handling Operations of CONano-micro letters · 2026Review
- Harnessing confinement effect and interpretable machine learning to predict alkane diffusion in zeolite catalysts.Nature communications · 2026Article
- Molecular Modeling-Based Machine Learning for Accurate Prediction of Gas Diffusivity and Permeability in Metal-Organic Frameworks.ACS materials Au · 2026Article
- Inverse Design of Metal-Organic Frameworks for CHAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- A Multi-Method Approach to Analyzing MOFs for Chemical Warfare Simulant Capture: Molecular Simulation, Machine Learning, and Molecular Fingerprints.Nanomaterials (Basel, Switzerland) · 2025Article
- Theoretical Study of Copper Squarate as a Promising Adsorbent for Small Gases Pollutants.Molecules (Basel, Switzerland) · 2024Article
- Topological Data Analysis Combined with High-Throughput Computational Screening of Hydrophobic Metal-Organic Frameworks: Application to the Adsorptive Separation of C3 Components.Nanomaterials (Basel, Switzerland) · 2024Article
- Accelerated Discovery of Metal-Organic Frameworks for COIndustrial & engineering chemistry research · 2024Review
- Interpretable Machine-Learning and Big Data Mining to Predict Gas Diffusivity in Metal-Organic Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 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
11 authors.
Funding
Abstract
For gas separation and catalysis by metal-organic frameworks (MOFs), gas diffusion has a substantial impact on the process' overall rate, so it is necessary to determine the molecular diffusion behavior within the MOFs. In this study, an interpretable machine learing (ML) model, light gradient boosting machine (LGBM), is trained to predict the molecular diffusivity and selectivity of 9 gases (Kr, Xe, CH
Indexed as
Identifiers
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.