ArticleACS omega2021
XGBoost: An Optimal Machine Learning Model with Just Structural Features to Discover MOF Adsorbents of Xe/Kr.
Article in ACS omega, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- RetNeXt: A Pretrained Model for Transfer Learning Across the MOF Adsorption Space.Journal of chemical information and modeling · 2026Article
- High-capacity removal of crystal violet using ZIF-8/graphene quantum dot composite with RSM optimization and explainable machine learning.Scientific reports · 2026Article
- Inverse Design of Metal-Organic Frameworks for CHAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Data-Driven Fouling Detection in Refinery Preheat Train Heat Exchangers Using Neural Networks and Gradient Boosting.Sensors (Basel, Switzerland) · 2025Article
- Integrating Molecular Simulations with Machine Learning to Discover Selective MOFs for CHThe journal of physical chemistry. C, Nanomaterials and interfaces · 2025Article
- Application of Soft Computing Represented by Regression Machine Learning Model and Artificial Lemming Algorithm in Predictions for Hydrogen Storage in Metal-Organic Frameworks.Materials (Basel, Switzerland) · 2025Article
- Mechanistic role of miR-375 in regulating PDPK1 to promote progression of small bowel neuroendocrine tumors: a silico analysis.Discover oncology · 2025Article
- Prediction Model for Risk of Death in Elderly Critically Ill Patients with Kidney Failure.Medicina (Kaunas, Lithuania) · 2025Article
- Evaluating CHACS applied materials & interfaces · 2025Review
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- Discovery of Dual Ion-Electron Conductivity of Metal-Organic Frameworks via Machine Learning-Guided Experimentation.Chemistry of materials : a publication of the American Chemical Society · 2025Article
- Scutellarin attenuated tubule cell apoptosis by modulating HIF-1α for the treatment of DKD: the insight integrating network analysis, machine learning and single-cell transcriptome.Frontiers in pharmacology · 2025Article
- Gut and respiratory microbiota landscapes in IgA nephropathy: a cross-sectional study.Renal failure · 2024Article
- Gas adsorption meets geometric deep learning: points, set and match.Scientific reports · 2024Article
- Understanding CO adsorption in MOFs combining atomic simulations and machine learning.Scientific reports · 2024Article
- Deep learning-based recommendation system for metal-organic frameworks (MOFs).Digital discovery · 2024Article
- Prediction of influenza outbreaks in Fuzhou, China: comparative analysis of forecasting models.BMC public health · 2024Article
- Rapid and Accurate Screening of the COF Space for Natural Gas Purification: COFInformatics.ACS applied materials & interfaces · 2024Article
- Systemic immune index predicts tumor-infiltrating lymphocyte intensity and immunotherapy response in small cell lung cancer.Translational lung cancer research · 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
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The inert gases Xe and Kr mainly exist in the used nuclear fuel (UNF) with the Xe/Kr ratio of 20:80, which it is difficult to separate. In this work, based on the G-MOFs database, high-throughput computational screening for metal-organic frameworks (MOFs) with high Xe/Kr adsorption selectivity was performed by combining grand canonical Monte Carlo (GCMC) simulations and machine learning (ML) technique for the first time. From the comparison of eight classical ML models, it is found that the XGBoost model with seven structural descriptors has superior accuracy in predicting the adsorption and separation performance of MOFs to Xe/Kr. Compared with energetic or electronic descriptors, structural descriptors are easier to obtain. Note that the determination coefficients
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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.