BetaCitation Atlas

RESEARCH PAPER · Coordination Chemistry Reviews · 2020

Applications of machine learning in metal-organic frameworks

Sanggyu Chong, Sangwon Lee, Baekjun Kim, Jihan Kim

Read original paper ↗

See where this research leads.

225 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
Filter Citing Papers
Press Enter or comma to add · × to remove · searches titles and available abstracts
Journals
All journals
Most represented first · select one or more
Explore Terms In These PapersHide SuggestionsShow Suggestions

Original and current center papers stay visible as anchors.
Numbers show citations received
42 direct citing works

Applications of machine learning in metal-organic frameworksOriginalHigh Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials127Persistent homology advances interpretable machine learning for nanoporous materials.4Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks22Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks1Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening49Stabilities and Electronic Structures of Transition Metal (Cu, Ag, Au, Ni, Pd, Pt) Cluster-Confined UiO-6618Two-dimensional conjugated microporous polymer films: fabrication strategies and potential applications45A Collection of More than 900 Gas Mixture Adsorption Experiments in Porous Materials from Literature Meta-Analysis69Bayesian optimization of nanoporous materials112Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches50Current Research Trends and Perspectives on Solid-State Nanomaterials in Hydrogen Storage135Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity40Insights into the estimation of capacitance for carbon-based supercapacitors58Metal–organic frameworks for chemical sensing devices327Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: from directional computational screening, machine learning to experiment48Occurrence and removal of antibiotics from industrial wastewater184Machine learning for advanced energy materials208Two-dimensional MOF and COF nanosheets for next-generation optoelectronic applications190Computational catalysis for metal-organic frameworks: An overview67Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties49A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning41Separation and Purification of Hydrocarbons with Porous Materials10Separation and Purification of Hydrocarbons with Porous Materials284Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery538Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks105Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks69Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation240A review on metal-organic frameworks photoelectrochemistry: A headlight for future applications148Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks45Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar158Topological methods for analysis and design of coordination polymers50Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials25High-Throughput Screening of Anion-Pillared Metal–Organic Frameworks for the Separation of Light Hydrocarbons25Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials0Integrated metal–organic framework and pressure/vacuum swing adsorption process design: Descriptor optimization26Modeling Multicomponent Gas Adsorption in Nanoporous Materials with Two Versions of Nonlocal Classical Density Functional Theory7Parametrization of Nonbonded Force Field Terms for Metal–Organic Frameworks Using Machine Learning Approach17Realizing the data-driven, computational discovery of metal-organic framework catalysts61MOFs in the time domain66Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption50A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen Storage3Advances in metal–organic framework-based membranes423Application of transfer learning to predict diffusion properties in metal–organic frameworks18Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks20Explainable graph neural networks for organic cages17Large-scale cascade cooling performance evaluation of adsorbent/water working pairs by integrated mathematical modelling and machine learning13Research Progresses of Metal-organic Framework HKUST-1-Based Membranes in Gas Separations※6Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling43MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis21A Hessian-based assessment of atomic forces for training machine learning interatomic potentials10Design of High-Humidity-Proof Hierarchical Porous P-ZIF-67(Co)-Polymer Composite Materials by Surface Modification for Highly Efficient Volatile Organic Compound Adsorption13Conductive Metal–Organic Frameworks for Supercapacitors325Graph neural network predictions of metal organic framework CO 2 adsorption properties93Deep-Learning-Based End-to-End Predictions of CO2 Capture in Metal–Organic Frameworks30Self-assembly of nanostructures with high complexity based on metal⋯unsaturated-bond coordination72High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration190Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models42Reverse-selective metal–organic framework materials for the efficient separation and purification of light hydrocarbons140Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-6656X-ray Diffraction and Molecular Simulations in the Study of Metal–Organic Frameworks for Membrane Gas Separation13The HEALED SBU Library of Chemically Realistic Building Blocks for Construction of Hypothetical Metal–Organic Frameworks24Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials45Machine learning prediction of open metal sites in metal-organic framework catalysts1Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges9MOFTransformer: A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks0Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture46Connecting the dots for fundamental understanding of structure–photophysics–property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism9A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation24A machine learning approach for predicting the performance of oxygen carriers in chemical looping oxidative coupling of methane9Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework3Machine learning transferable atomic forces for large systems from underconverged molecular fragments12Machine learning-assisted prediction of water adsorption isotherms and cooling performance18Synthesis, characterization & catalysis of ITQ 2D metal–organic frameworks and spectroscopic & photodynamic properties of their composites with organic dyes5Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening3Machine Learning‐Aided Discovery of Nanoporous Materials for Energy‐ and Environmental‐Related Applications4Analysis and evaluation of machine learning applications in materials design and discovery33Automated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal–Organic Frameworks18Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis94A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks208Recent advances in computational modeling of MOFs: From molecular simulations to machine learning217Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset33Artificial intelligence assisted with designing metal‐organic frameworks (MOFs)17High-throughput and machine learning approaches for the discovery of metal organic frameworks18Materials cartography: A forward-looking perspective on materials representation and devising better maps13Functional Material Systems Enabled by Automated Data Extraction and Machine Learning33Automatic Machine Learning Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks for Capture of Methanol and Ethanol from the Air12A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning16Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology2Phosphate adsorption by metal organic frameworks: Insights from a systematic review, meta-analysis, and predictive modelling with artificial neural networks40Prediction of Adsorptive Activities of MOFs for Pollutants in Aqueous Phase Based on Machine Learning20Efficient Exploration of Adsorption Space for Separations in Metal–Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory14Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks36Connectivity Analysis of Adsorption Sites in Metal–Organic Frameworks for Facilitated Water Adsorption16Host–guest interactions in framework materials: Insight from modeling28Application of Digital Methods in Polymer Science and Engineering28Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective43Predicting Gas Adsorption without the Knowledge of Pore Structures: A Machine Learning Method Based on Classical Density Functional Theory3ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs168Application of machine learning in MOFs for gas adsorption and separation31MOF-GRU: A MOFid-Aided Deep Learning Model for Predicting the Gas Separation Performance of Metal–Organic Frameworks20Membranes for CO2 capture and separation: Progress in research and development for industrial applications217Metal‐Organic Framework (MOF)‐Based Clean Energy Conversion: Recent Advances in Unlocking its Underlying Mechanisms60Drug delivery system tailoring via metal-organic framework property prediction using machine learning: A disregarded approach20Computational screening of metal–organic frameworks for environmental protection0Deep learning-based recommendation system for metal–organic frameworks (MOFs)15Prediction of Carbon Dioxide Adsorption on Metal-Organic Frameworks: A Combined Machine Learning and Optimization Approach0Unifying Mixed Gas Adsorption in Molecular Sieve Membranes and Mofs Using Machine Learning1Machine learning for polyphenol-based materials10Predictive transport modelling in polymeric gas separation membranes: From additive contributions to machine learning19Prediction of Hydrogen Adsorption and Moduli of Metal–Organic Frameworks (MOFs) Using Machine Learning Strategies26Probing Structural Defects in MOFs Using Water Stability34From Synthesis Conditions to UiO-66 Properties: Machine Learning Approach15WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models2WS24: A diverse data set for predicting metal-organic framework stability in water and harsh environments with data-driven models1Machine learning: An accelerator for the exploration and application of advanced metal-organic frameworks46Combining machine learning and metal–organic frameworks research: Novel modeling, performance prediction, and materials discovery76Unified Roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and Their Impact on Particle Size and Morphology48Linear regression model for metal–organic frameworks with CO2 adsorption based on topological data analysis8Engineering Machine Learning Features to Predict Adsorption of Carbon Dioxide and Nitrogen in Metal–Organic Frameworks33Different applications of machine learning approaches in materials science and engineering: Comprehensive review68Unifying mixed gas adsorption in molecular sieve membranes and MOFs using machine learning15Classifying and Predicting the Thermal Expansion Properties of Metal–Organic Frameworks: A Data-Driven Approach9Machine Learning Assisted Discovery of Efficient MOFs for One-Step C2H4 Purification from Ternary C2H2/C2H4/C2H6 Mixtures3Current update and prospects in the development of conductive metal-organic framework electrodes for lithium-based batteries13Efficient Removal of Greenhouse Gases: Machine Learning-Assisted Exploration of Metal–Organic Framework Space32Metal–Organic Framework Stability in Water and Harsh Environments from Data-Driven Models Trained on the Diverse WS24 Data Set88Charting a Path to Tumor Therapy: Mastery of Luminescent Metal–Organic Frameworks for Precise Spatiotemporal Control19Combining machine learning and molecular simulation to explore MOF materials that contribute to CF4/N2 separation9Nano-enhanced solid-state hydrogen storage: Balancing discovery and pragmatism for future energy solutions24Broad Range Material-to-System Screening of Metal-Organic Frameworks for Hydrogen Storage Using Machine Learning1Molecular simulations and deep neural networks‐based interpretable machine learning modelling of reverse adsorptive MOFs for ethane/ethylene separation3A universal machine learning framework to automatically identify high‐performance covalent organic framework membranes for CH 4 /H 2 separation15Module-based machine learning models using sigma profiles of organic linkers to predict gaseous adsorption in metal-organic frameworks20Overcoming the Barriers to Machine Learning Adoption in External Auditing: Insights from Auditors' Perspectives1From Data to Discovery: Recent Trends of Machine Learning in Metal–Organic Frameworks91Optimizing the prediction of adsorption in metal–organic frameworks leveraging Q‐learning12Application of artificial intelligence in the materials science, with a special focus on fuel cells and electrolyzers78Graphitic carbon nitride composites as advanced versatile materials for adsorption and photocatalytic degradation of emerging pollutants from wastewater29Multi-Scale Computational Design of Metal–Organic Frameworks for Carbon Capture Using Machine Learning and Multi-Objective Optimization29Metal–Organic Frameworks through the Lens of Artificial Intelligence: A Comprehensive Review46Machine learning applications in nanomaterials: Recent advances and future perspectives94Machine learning of metal-organic framework design for carbon dioxide capture and utilization28Computational design of Metal-Organic Frameworks for sustainable energy and environmental applications: Bridging theory and experiment6Metal-organic framework for hydrogen storage: Advances and challenges brought by the new technologies27A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste13Novel amino-functionalized MOF-based sensor for zinc ion detection in water and blood serum samples28Machine learning for gas–solid interaction materials and devices10Strategic Insights into Perovskite-MOF Hybrids: Tailoring Photocatalysts for Sustainable Solutions9Robust machine-learning model for prediction of carbon dioxide adsorption on metal-organic frameworks30

Arrows point to the paper being cited. Selecting a paper shows its incoming citations and its known path back to the original paper.

Drag to pan · Use + / − to zoom · Escape to close43 nodes / 53 citation links
Browse Matching Papers

Papers that cite this work

225 direct citing papers saved. Showing the first 50 here; use Browse Matching Papers in the map for all loaded papers.

  1. High Throughput Methods in the Synthesis, Characterization, and Optimization of Porous Materials

    Advanced Materials · 2020-09-21

    Ivan G. Clayson, Daniel Hewitt, Martin Hutereau, Tom Pope, Ben Slater

    Read original paper ↗ ·

  2. Persistent homology advances interpretable machine learning for nanoporous materials.

    arXiv (Cornell University) · 2020-10-01

    Aditi S. Krishnapriyan, Joseph H. Montoya, Jens S. Hummelshøj, Dmitriy Morozov

    Read original paper ↗ ·

  3. Towards a machine learned thermodynamics: exploration of free energy landscapes in molecular fluids, biological systems and for gas storage and separation in metal–organic frameworks

    Molecular Systems Design & Engineering · 2020-11-23

    Caroline Desgranges, Jérôme Delhommelle

    Read original paper ↗ ·

  4. Towards explainable message passing networks for predicting carbon dioxide adsorption in metal-organic frameworks

    arXiv (Cornell University) · 2020-12-02

    Ali Raza, Faaiq Waqar, Árni Sturluson, Cory M. Simon, Xiaoli Z. Fern

    Read original paper ↗ ·

  5. Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening

    The Journal of Physical Chemistry C · 2020-12-07

    Eun Hyun Cho, Xuepeng Deng, Changlong Zou, Li‐Chiang Lin

    Read original paper ↗ ·

  6. Stabilities and Electronic Structures of Transition Metal (Cu, Ag, Au, Ni, Pd, Pt) Cluster-Confined UiO-66

    The Journal of Physical Chemistry C · 2020-12-10

    Fangfang Wang, Liang Feng, Yunyi Zhang, Xujian Chen, De‐Li Chen, Weidong Zhu

    Read original paper ↗ ·

  7. Two-dimensional conjugated microporous polymer films: fabrication strategies and potential applications

    Polymer Chemistry · 2020-12-27

    Zhengdong Liu, Yuhang Yin, Mustafa Eginligil, Laiyuan Wang, Juqing Liu, Wei Huang

    Read original paper ↗ ·

  8. A Collection of More than 900 Gas Mixture Adsorption Experiments in Porous Materials from Literature Meta-Analysis

    Industrial & Engineering Chemistry Research · 2020-12-31

    Xuqing Cai, Farhad Gharagheizi, Lukas W. Bingel, Danny Shade, Krista S. Walton, David S. Sholl

    Read original paper ↗ ·

  9. Bayesian optimization of nanoporous materials

    Molecular Systems Design & Engineering · 2021-01-01

    Aryan Deshwal, Cory M. Simon, Janardhan Rao Doppa

    Read original paper ↗ ·

  10. Computational techniques for characterisation of electrically conductive MOFs: quantum calculations and machine learning approaches

    Journal of Materials Chemistry C · 2021-01-01

    Federica Zanca, Lawson T. Glasby, Sanggyu Chong, Siyu Chen, Jihan Kim, David Fairen‐Jiménez, Bartomeu Monserrat, Peyman Z. Moghadam

    Read original paper ↗ ·

  11. Current Research Trends and Perspectives on Solid-State Nanomaterials in Hydrogen Storage

    Research · 2021-01-01

    Jie Zheng, Chen‐Gang Wang, Hui Wen Zhou, Enyi Ye, Jianwei Xu, Zibiao Li, Xian Jun Loh

    Read original paper ↗ ·

  12. Finely tuned inverse design of metal–organic frameworks with user-desired Xe/Kr selectivity

    Journal of Materials Chemistry A · 2021-01-01

    Yunsung Lim, Junkil Park, Sangwon Lee, Jihan Kim

    Read original paper ↗ ·

  13. Insights into the estimation of capacitance for carbon-based supercapacitors

    RSC Advances · 2021-01-01

    Majedeh Gheytanzadeh, Alireza Baghban, Sajjad Habibzadeh, Ahmad Mohaddespour, Otman Abida

    Read original paper ↗ ·

  14. Metal–organic frameworks for chemical sensing devices

    Materials Horizons · 2021-01-01

    Joseph Funso Olorunyomi, Shu Teng Geh, Rachel A. Caruso, Cara M. Doherty

    Read original paper ↗ ·

  15. Techno-economic analysis of metal–organic frameworks for adsorption heat pumps/chillers: from directional computational screening, machine learning to experiment

    Journal of Materials Chemistry A · 2021-01-01

    Zenan Shi, Xueying Yuan, Yaling Yan, Yuanlin Tang, Junjie Li, Hong Liang, Lianpeng Tong, Zhiwei Qiao

    Read original paper ↗ ·

  16. Occurrence and removal of antibiotics from industrial wastewater

    Environmental Chemistry Letters · 2021-01-03

    Dilipkumar Akhil, Divya Lakshmi, P. Senthil Kumar, Dai‐Viet N. Vo, Ashokkumar Kartik

    Read original paper ↗ ·

  17. Machine learning for advanced energy materials

    Energy and AI · 2021-01-24

    Liu Yun, Oladapo Christopher Esan, Zhefei Pan, Liang An

    Read original paper ↗ ·

  18. Two-dimensional MOF and COF nanosheets for next-generation optoelectronic applications

    Coordination Chemistry Reviews · 2021-02-18

    Xiaohui Ren, Gengcheng Liao, Zhongjun Li, Hui Qiao, Yuan Zhang, Xiang Yu, Bing Wang, Hui Feng Tan, Li Shi, Xiang Qi, Han Zhang

    Read original paper ↗ ·

  19. Computational catalysis for metal-organic frameworks: An overview

    Coordination Chemistry Reviews · 2021-02-24

    Gavin A. McCarver, Thayalan Rajeshkumar, Konstantinos D. Vogiatzis

    Read original paper ↗ ·

  20. Nanoporous Material Recognition via 3D Convolutional Neural Networks: Prediction of Adsorption Properties

    The Journal of Physical Chemistry Letters · 2021-03-01

    Eun Hyun Cho, Li‐Chiang Lin

    Read original paper ↗ ·

  21. A Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    ChemRxiv · 2021-03-16

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  22. Adsorption behavior of metal-organic frameworks: From single simulation, high-throughput computational screening to machine learning

    Computational Materials Science · 2021-03-23

    Yaling Yan, Lulu Zhang, Shuhua Li, Hong Liang, Zhiwei Qiao

    Read original paper ↗ ·

  23. Separation and Purification of Hydrocarbons with Porous Materials

    Angewandte Chemie · 2021-03-30

    Yaqi Wu, Bert Marc Weckhuysen

    Read original paper ↗ ·

  24. Separation and Purification of Hydrocarbons with Porous Materials

    Angewandte Chemie International Edition · 2021-03-30

    Yaqi Wu, Bert Marc Weckhuysen

    Read original paper ↗ ·

  25. Machine learning the quantum-chemical properties of metal–organic frameworks for accelerated materials discovery

    Matter · 2021-04-05

    Andrew Rosen, Shaelyn M. Iyer, Debmalya Ray, Zhenpeng Yao, Alán Aspuru‐Guzik, Laura Gagliardi, Justin M. Notestein, Randall Q. Snurr

    Read original paper ↗ ·

  26. Discovering Relationships between OSDAs and Zeolites through Data Mining and Generative Neural Networks

    ACS Central Science · 2021-04-16

    Zach Jensen, Soonhyoung Kwon, Daniel Schwalbe‐Koda, Cecilia Paris, Rafael Gómez‐Bombarelli, Yuriy Román‐Leshkov, Avelino Corma, Manuel Moliner, Elsa Olivetti

    Read original paper ↗ ·

  27. Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks

    Scientific Reports · 2021-04-26

    Aditi S. Krishnapriyan, Joseph H. Montoya, Maciej Harańczyk, Jens S. Hummelshøj, Dmitriy Morozov

    Read original paper ↗ ·

  28. Machine Learning Meets with Metal Organic Frameworks for Gas Storage and Separation

    Journal of Chemical Information and Modeling · 2021-04-29

    Çiğdem Altıntaş, Ömer Faruk Altundal, Seda Keskın, Ramazan Yıldırım

    Read original paper ↗ ·

  29. A review on metal-organic frameworks photoelectrochemistry: A headlight for future applications

    Coordination Chemistry Reviews · 2021-06-29

    Kheibar Dashtian, Saeed Shahbazi, Meysam Tayebi, Zohreh Masoumi

    Read original paper ↗ ·

  30. Efficient Models for Predicting Temperature-Dependent Henry’s Constants and Adsorption Selectivities for Diverse Collections of Molecules in Metal–Organic Frameworks

    The Journal of Physical Chemistry C · 2021-08-11

    Xiaohan Yu, Sihoon Choi, Dai Tang, Andrew James Medford, David S. Sholl

    Read original paper ↗ ·

  31. Artificial intelligence (AI) applications in adsorption of heavy metals using modified biochar

    The Science of The Total Environment · 2021-08-18

    Divya Lakshmi, Dilipkumar Akhil, Ashokkumar Kartik, Kannappan Panchamoorthy Gopinath, Jayaseelan Arun, Amit Bhatnagar, Jörg Rinklebe, Woong Kim, Muthusamy Govarthanan

    Read original paper ↗ ·

  32. Topological methods for analysis and design of coordination polymers

    Russian Chemical Reviews · 2021-08-23

    Eugeny V. Alexandrov, Alexander P. Shevchenko, Nadezhda A. Nekrasova, Vladislav A. Blatov

    Read original paper ↗ ·

  33. Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    Chemistry of Materials · 2021-09-07

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  34. High-Throughput Screening of Anion-Pillared Metal–Organic Frameworks for the Separation of Light Hydrocarbons

    The Journal of Physical Chemistry C · 2021-09-08

    Chenkai Gu, Jing Liu, David S. Sholl

    Read original paper ↗ ·

  35. Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials

    Unknown journal · 2021-10-26

    Árni Sturluson, Ali Raza, Grant D. McConachie, Daniel W. Siderius, Xiaoli Z. Fern, Cory M. Simon

    Read original paper ↗ ·

  36. Integrated metal–organic framework and pressure/vacuum swing adsorption process design: Descriptor optimization

    AIChE Journal · 2021-11-05

    Xiang Zhang, Teng Zhou, Kai Sundmacher

    Read original paper ↗ ·

  37. Modeling Multicomponent Gas Adsorption in Nanoporous Materials with Two Versions of Nonlocal Classical Density Functional Theory

    Industrial & Engineering Chemistry Research · 2021-11-13

    Musen Zhou, Jingqi Wang, Jose Garcia, Yu Liu, Jianzhong Wu

    Read original paper ↗ ·

  38. Parametrization of Nonbonded Force Field Terms for Metal–Organic Frameworks Using Machine Learning Approach

    Journal of Chemical Information and Modeling · 2021-11-17

    Vadim Korolev, Yuriy M. Nevolin, Thomas A. Manz, Pavel V. Protsenko

    Read original paper ↗ ·

  39. Realizing the data-driven, computational discovery of metal-organic framework catalysts

    Current Opinion in Chemical Engineering · 2021-11-22

    Andrew Rosen, Justin M. Notestein, Randall Q. Snurr

    Read original paper ↗ ·

  40. MOFs in the time domain

    Nature Reviews Chemistry · 2021-12-02

    Daniel J. Cerasale, Dominic C. Ward, Timothy L. Easun

    Read original paper ↗ ·

  41. Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption

    Microporous and Mesoporous Materials · 2021-12-30

    Ruihan Wang, Yurong Zou, Chunchun Zhang, Xin Wang, Mingli Yang, Dingguo Xu

    Read original paper ↗ ·

  42. A New Machine Learning Framework for Efficient MOF Discovery: Application to Hydrogen Storage

    Computer-aided chemical engineering/Computer aided chemical engineering · 2022-01-01

    Teng Zhou, Zihao Wang, Kai Sundmacher

    Read original paper ↗ ·

  43. Advances in metal–organic framework-based membranes

    Chemical Society Reviews · 2022-01-01

    Youdong Cheng, Shuvo Jit Datta, Sheng Zhou, Jiangtao Jia, Osama Shekhah, Mohamed Eddaoudi

    Read original paper ↗ ·

  44. Application of transfer learning to predict diffusion properties in metal–organic frameworks

    Molecular Systems Design & Engineering · 2022-01-01

    Yunsung Lim, Jihan Kim

    Read original paper ↗ ·

  45. Deep learning neural network potential for simulating gaseous adsorption in metal–organic frameworks

    Materials Advances · 2022-01-01

    Chi‐Ta Yang, Ishan Pandey, Dan Trinh, Chau‐Chyun Chen, Joshua D. Howe, Li‐Chiang Lin

    Read original paper ↗ ·

  46. Explainable graph neural networks for organic cages

    Digital Discovery · 2022-01-01

    Qi Yuan, Filip Szczypiński, Kim E. Jelfs

    Read original paper ↗ ·

  47. Large-scale cascade cooling performance evaluation of adsorbent/water working pairs by integrated mathematical modelling and machine learning

    Journal of Materials Chemistry A · 2022-01-01

    Zhilu Liu, Wei Hao Li, Shanshan Cai, Zhengkai Tu, Xiaobing Luo, Song Li

    Read original paper ↗ ·

  48. Research Progresses of Metal-organic Framework HKUST-1-Based Membranes in Gas Separations※

    化学学报 · 2022-01-01

    Chong Li, Na Li, Li‐Mei Chang, Zhi‐Gang Gu, Jian Zhang

    Read original paper ↗ ·

  49. Identification of optimal metal-organic frameworks by machine learning: Structure decomposition, feature integration, and predictive modeling

    Computers & Chemical Engineering · 2022-02-17

    Zihao Wang, Yageng Zhou, Teng Zhou, Kai Sundmacher

    Read original paper ↗ ·

  50. MOFSocialNet: Exploiting Metal-Organic Framework Relationships via Social Network Analysis

    Nanomaterials · 2022-02-20

    Mehrdad Jalali, Manuel M. Tsotsalas, Christof Wöll

    Read original paper ↗ ·
More citing papers →
Citation Atlas · A map of research, not a ranking.