Papers citing Applications of machine learning in metal-organic frameworks
A Hessian-based assessment of atomic forces for training machine learning interatomic potentials
The Journal of Chemical Physics · 2022-02-28
Marius Herbold, Jörg Behler
Read original paper ↗ ·Design of High-Humidity-Proof Hierarchical Porous P-ZIF-67(Co)-Polymer Composite Materials by Surface Modification for Highly Efficient Volatile Organic Compound Adsorption
Industrial & Engineering Chemistry Research · 2022-03-04
Hao Wu, Pu Wang, Le Du, Junsu Jin, Jianguo Mi, Jimmy Yun
Read original paper ↗ ·Conductive Metal–Organic Frameworks for Supercapacitors
Advanced Materials · 2022-03-31
Liang Niu, Taizheng Wu, Ming Chen, Long Yang, Jingjing Yang, Zhenxiang Wang, Alexei A. Kornyshev, Huili Jiang, Sheng Bi, Guang Feng
Read original paper ↗ ·Graph neural network predictions of metal organic framework CO 2 adsorption properties
Computational Materials Science · 2022-04-14
Kamal Kumar Choudhary, Taner Yildirim, Daniel W. Siderius, Aaron Gilad Kusne, Austin McDannald, Diana L. Ortiz‐Montalvo
Read original paper ↗ ·Deep-Learning-Based End-to-End Predictions of CO2 Capture in Metal–Organic Frameworks
Journal of Chemical Information and Modeling · 2022-05-16
Cunxing Lu, Xili Wan, Xuhao Ma, Xinjie Guan, Aichun Zhu
Read original paper ↗ ·Self-assembly of nanostructures with high complexity based on metal⋯unsaturated-bond coordination
Coordination Chemistry Reviews · 2022-05-16
Yuya Domoto, Makoto Fujita
Read original paper ↗ ·High-throughput predictions of metal–organic framework electronic properties: theoretical challenges, graph neural networks, and data exploration
npj Computational Materials · 2022-05-17
Andrew Rosen, Victor Fung, Patrick Huck, Cody T. O’Donnell, Matthew K. Horton, Donald G. Truhlar, Kristin Aslaug Persson, Justin M. Notestein, Randall Q. Snurr
Read original paper ↗ ·Repurposing N-Doped Grape Marc for the Fabrication of Supercapacitors with Theoretical and Machine Learning Models
Nanomaterials · 2022-05-27
Kethaki Wickramaarachchi, Manickam Minakshi, S. Assa Aravindh, Rukshima Dabare, Xiangpeng Gao, Zhong‐Tao Jiang, Kok Wai Wong
Read original paper ↗ ·Reverse-selective metal–organic framework materials for the efficient separation and purification of light hydrocarbons
Coordination Chemistry Reviews · 2022-05-27
Shan‐Qing Yang, Tong‐Liang Hu
Read original paper ↗ ·Combined Deep Learning and Classical Potential Approach for Modeling Diffusion in UiO-66
Journal of Chemical Theory and Computation · 2022-06-02
Siddarth Krishnaraja Achar, Jacob J. Wardzala, Leonardo Bernasconi, Linfeng Zhang, J. Karl Johnson
Read original paper ↗ ·X-ray Diffraction and Molecular Simulations in the Study of Metal–Organic Frameworks for Membrane Gas Separation
Langmuir · 2022-07-26
Dun‐Yen Kang, Jong Suk Lee, Li‐Chiang Lin
Read original paper ↗ ·The HEALED SBU Library of Chemically Realistic Building Blocks for Construction of Hypothetical Metal–Organic Frameworks
ACS Applied Materials & Interfaces · 2022-09-19
Marco Gibaldi, Ohmin Kwon, Andrew White, Jake Burner, Tom K. Woo
Read original paper ↗ ·Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials
Nanomaterials · 2022-09-27
Wenjing Guo, Jie Liu, Fan Dong, Ru Chen, Jayanti Das, Weigong Ge, Xiaoming Xu, Huixiao Hong
Read original paper ↗ ·Machine learning prediction of open metal sites in metal-organic framework catalysts
ChemRxiv · 2022-10-10
Huixuan Guo, Jiahao Ma, Yueshan Zhong, Yuhui Yin, Mingcan Chen
Read original paper ↗ ·Computational discovery of Metal–Organic Frameworks for sustainable energy systems: Open challenges
Computers & Chemical Engineering · 2022-10-14
Xiangyu Yin, Chrysanthos E. Gounaris
Read original paper ↗ ·MOFTransformer: A Multi-modal Pre-training Transformer for Universal Transfer Learning in Metal-Organic Frameworks
ChemRxiv · 2022-10-20
Yeonghun Kang, Hyunsoo Park, Berend Smit, Jihan Kim
Read original paper ↗ ·Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture
Advanced Science · 2022-10-26
Haoxin Mai, Tu C. Le, Dehong Chen, David Alan Winkler, Rachel A. Caruso
Read original paper ↗ ·Connecting the dots for fundamental understanding of structure–photophysics–property relationships of COFs, MOFs, and perovskites using a Multiparticle Holstein Formalism
Chemical Science · 2022-11-16
Raja Ghosh, Francesco Paesani
Read original paper ↗ ·A State-of-the-Art of Metal-Organic Frameworks for Chromium Photoreduction vs. Photocatalytic Water Remediation
Nanomaterials · 2022-11-30
Andreína García, Bárbara Rodríguez, Maibelín Rosales, Yurieth Marcela Quintero, Paula G. Saiz, Ander Reizabal, Stefan Wuttke, Leire Celaya-Azcoaga, Ainara Valverde, Roberto Fernández de Luis
Read original paper ↗ ·A machine learning approach for predicting the performance of oxygen carriers in chemical looping oxidative coupling of methane
Sustainable Energy & Fuels · 2023-01-01
Dewang Zeng, Yiwen Song, Mengmeng Wang, Yingjie Lu, Zehua Chen, Rui Xiao
Read original paper ↗ ·Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework
Computational methods in engineering & the sciences · 2023-01-01
Wenjing Guo, Jie Liu, Fan Dong, Tucker A. Patterson, Huixiao Hong
Read original paper ↗ ·Machine learning transferable atomic forces for large systems from underconverged molecular fragments
Physical Chemistry Chemical Physics · 2023-01-01
Marius Herbold, Jörg Behler
Read original paper ↗ ·Machine learning-assisted prediction of water adsorption isotherms and cooling performance
Journal of Materials Chemistry A · 2023-01-01
Zhilu Liu, Dongchen Shen, Shanshan Cai, Zhengkai Tu, Song Li
Read original paper ↗ ·Synthesis, characterization & catalysis of ITQ 2D metal–organic frameworks and spectroscopic & photodynamic properties of their composites with organic dyes
Journal of Materials Chemistry C · 2023-01-01
Mario Gutiérrez, Urbano Díaz, Boiko Cohen, Abderrazzak Douhal
Read original paper ↗ ·Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening
Industrial & Engineering Chemistry Research · 2023-01-18
Shubham Jamdade, Rishi Gurnani, Hanjun Fang, Salah Eddine Boulfelfel, Rampi Ramprasad, David S. Sholl
Read original paper ↗ ·Machine Learning‐Aided Discovery of Nanoporous Materials for Energy‐ and Environmental‐Related Applications
Unknown journal · 2023-01-24
Archit Datar, Qiang Lyu, Li‐Chiang Lin
Read original paper ↗ ·Analysis and evaluation of machine learning applications in materials design and discovery
Materials Today Communications · 2023-01-25
Mahsa Golmohammadi, Masoud Aryanpour
Read original paper ↗ ·Automated Graph Neural Networks Accelerate the Screening of Optoelectronic Properties of Metal–Organic Frameworks
The Journal of Physical Chemistry Letters · 2023-01-30
Zhaosheng Zhang
Read original paper ↗ ·Machine learning accelerates the investigation of targeted MOFs: Performance prediction, rational design and intelligent synthesis
Nano Today · 2023-03-10
Jing Lin, Zhimeng Liu, Yujie Guo, Shulin Wang, Tao Zhang, Xiangdong Xue, Rushuo Li, Shihao Feng, Linmeng Wang, Jiangtao Liu, Hongyi Gao, Ge Wang, Yanjing Su
Read original paper ↗ ·A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks
Nature Machine Intelligence · 2023-03-13
Yeonghun Kang, Hyunsoo Park, Berend Smit, Jihan Kim
Read original paper ↗ ·Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
Coordination Chemistry Reviews · 2023-03-21
Hakan Demir, Hilal Daglar, Hasan Can Gülbalkan, Gokhan Onder Aksu, Seda Keskın
Read original paper ↗ ·Analyzing of metal organic frameworks performance in CH4 adsorption using machine learning techniques: A GBRT model based on small training dataset
Journal of environmental chemical engineering · 2023-05-09
Xin Wei, Ding Peng, Lin Fang Shen, Yuejie Ai, Zhanhui Lu
Read original paper ↗ ·Artificial intelligence assisted with designing metal‐organic frameworks (MOFs)
Clinical and Translational Discovery · 2023-06-01
Navid Rabiee
Read original paper ↗ ·High-throughput and machine learning approaches for the discovery of metal organic frameworks
APL Materials · 2023-06-01
Xiangyu Zhang, Zezhao Xu, Zidi Wang, Huiyu Liu, Yingbo Zhao, Shan Hao Jiang
Read original paper ↗ ·Materials cartography: A forward-looking perspective on materials representation and devising better maps
APL Machine Learning · 2023-06-01
Steven B. Torrisi, Martin Z. Bazant, Alexander E. Cohen, Min Gee Cho, Jens S. Hummelshøj, Linda Hung, Gaurav Ashish Kamat, Arash Khajeh, Adeesh Kolluru, Xiangyun Lei, Handong Ling, Joseph H. Montoya, Tim Mueller, Aini Palizhati, Benjamin Paren, Brandon K. Phan, Jacob Pietryga, Elodie Sandraz, Daniel Schweigert, Yang Shao‐Horn, Amalie Trewartha, Ruijie Zhu, Debbie Zhuang, Shijing Sun
Read original paper ↗ ·Functional Material Systems Enabled by Automated Data Extraction and Machine Learning
Advanced Functional Materials · 2023-06-05
Payam Kalhor, Nicole Jung, Stefan Bräse, Christof Wöll, Manuel M. Tsotsalas, Pascal Friederich
Read original paper ↗ ·Automatic Machine Learning Combined with High-Throughput Computational Screening of Hydrophobic Metal–Organic Frameworks for Capture of Methanol and Ethanol from the Air
ACS ES&T Engineering · 2023-07-03
Lulu Zhang, Qiuhong Huang, Lifeng Li, Yaling Yan, Xueying Yuan, Hong Liang, Shuhua Li, Bangfen Wang, Zhiwei Qiao
Read original paper ↗ ·A large-scale screening of metal-organic frameworks for iodine capture combining molecular simulation and machine learning
Frontiers of Environmental Science & Engineering · 2023-07-15
Min Cheng, Zhiyuan Zhang, Shihui Wang, Kexin Bi, Kong‐Qiu Hu, Zhongde Dai, Yiyang Dai, Chong Liu, Li Zhou, Xu Ji, Wei‐Qun Shi
Read original paper ↗ ·Unified roadmap for ZIF-8 Nucleation and Growth: Machine Learning Analysis of Synthetic Variables and their Impact on Particle Size and Morphology
ChemRxiv · 2023-07-19
Juan A. Allegretto, Diego Onna, Laura Ación, Sara A. Bilmes, Omar Azzaroni, Matías Rafti
Read original paper ↗ ·Phosphate adsorption by metal organic frameworks: Insights from a systematic review, meta-analysis, and predictive modelling with artificial neural networks
Chemosphere · 2023-07-28
Giovani Rodolfo Alatrista Gongora, Chris Pratt, Ali El Hanandeh
Read original paper ↗ ·Prediction of Adsorptive Activities of MOFs for Pollutants in Aqueous Phase Based on Machine Learning
ACS ES&T Engineering · 2023-08-15
Jiahao Li, Jiawei Wang, Hongxin Mu, Haidong Hu, Jinfeng Wang, Hongqiang Ren, Bing Wu
Read original paper ↗ ·Efficient Exploration of Adsorption Space for Separations in Metal–Organic Frameworks Combining the Use of Molecular Simulations, Machine Learning, and Ideal Adsorbed Solution Theory
The Journal of Physical Chemistry C · 2023-09-14
Xiaohan Yu, Dai Tang, Jia Yuan Chng, David S. Sholl
Read original paper ↗ ·Rapid design of top-performing metal-organic frameworks with qualitative representations of building blocks
npj Computational Materials · 2023-09-21
Yigitcan Comlek, Thang Duc Pham, Randall Q. Snurr, Wei Chen
Read original paper ↗ ·Connectivity Analysis of Adsorption Sites in Metal–Organic Frameworks for Facilitated Water Adsorption
ACS Applied Materials & Interfaces · 2023-09-27
Zhi-Xun Xu, Yi-Ming Wang, Li‐Chiang Lin
Read original paper ↗ ·Host–guest interactions in framework materials: Insight from modeling
Chemical Physics Reviews · 2023-10-20
Michelle Ernst, Jack D. Evans, Ganna Gryn’ova
Read original paper ↗ ·Application of Digital Methods in Polymer Science and Engineering
Advanced Functional Materials · 2023-10-27
Timo Schuett, Patrick Endres, Tobias Standau, Stefan Zechel, Rodrigo Queiroz Albuquerque, Christian Brütting, Holger Ruckdäschel, Ulrich S. Schubert
Read original paper ↗ ·Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
Langmuir · 2023-11-03
Hongjian Tang, Lunbo Duan, Jianwen Jiang
Read original paper ↗ ·Predicting Gas Adsorption without the Knowledge of Pore Structures: A Machine Learning Method Based on Classical Density Functional Theory
The Journal of Physical Chemistry Letters · 2023-11-03
Xiangkun Wu, Yu Liu
Read original paper ↗ ·ChatGPT Research Group for Optimizing the Crystallinity of MOFs and COFs
ACS Central Science · 2023-11-10
Zhiling Zheng, Oufan Zhang, Ha Lac Nguyen, Nakul Rampal, Ali H. Alawadhi, Zichao Rong, Teresa L. Head-Gordon, Christian Borgs, Jennifer Chayes, Omar M. Yaghi
Read original paper ↗ ·Application of machine learning in MOFs for gas adsorption and separation
Materials Research Express · 2023-11-13
Chao He Yang, Jingjing Qi, Anquan Wang, Jingyu Zha, Chao Liu, Shupeng Yao
Read original paper ↗ ·