标题
Leveraging Machine Learning for Metal–Organic Frameworks: A Perspective
作者
关键词
-
出版物
LANGMUIR
Volume -, Issue -, Pages -
出版商
American Chemical Society (ACS)
发表日期
2023-11-04
DOI
10.1021/acs.langmuir.3c01964
参考文献
相关参考文献
注意:仅列出部分参考文献,下载原文获取全部文献信息。- ARC–MOF: A Diverse Database of Metal-Organic Frameworks with DFT-Derived Partial Atomic Charges and Descriptors for Machine Learning
- (2023) Jake Burner et al. Chemistry of Materials
- MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
- (2023) Zhonglin Cao et al. Journal of the American Chemical Society
- Machine learning potentials for metal-organic frameworks using an incremental learning approach
- (2023) Sander Vandenhaute et al. npj Computational Materials
- Quantum Informed Machine-Learning Potentials for Molecular Dynamics Simulations of CO2’s Chemisorption and Diffusion in Mg-MOF-74
- (2023) Bowen Zheng et al. ACS Nano
- DigiMOF: A Database of Metal–Organic Framework Synthesis Information Generated via Text Mining
- (2023) Lawson T. Glasby et al. Chemistry of Materials
- Recent advances in computational modeling of MOFs: From molecular simulations to machine learning
- (2023) Hakan Demir et al. COORDINATION CHEMISTRY REVIEWS
- Metal–Organic Frameworks for Water Harvesting: Machine Learning-Based Prediction and Rapid Screening
- (2023) Zhiming Zhang et al. ACS Sustainable Chemistry & Engineering
- An Ecosystem for Digital Reticular Chemistry
- (2023) Kevin Maik Jablonka et al. ACS Central Science
- A multi-modal pre-training transformer for universal transfer learning in metal–organic frameworks
- (2023) Yeonghun Kang et al. Nature Machine Intelligence
- MOF Synthesis Prediction Enabled by Automatic Data Mining and Machine Learning
- (2022) Yi Luo et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Data-Driven Matching of Experimental Crystal Structures and Gas Adsorption Isotherms of Metal–Organic Frameworks
- (2022) Daniele Ongari et al. JOURNAL OF CHEMICAL AND ENGINEERING DATA
- Introducing artificial MOFs for improved machine learning predictions: Identification of top-performing materials for methane storage
- (2022) George S. Fanourgakis et al. JOURNAL OF CHEMICAL PHYSICS
- Thermal Stability of Metal–Organic Frameworks (MOFs): Concept, Determination, and Model Prediction Using Computational Chemistry and Machine Learning
- (2022) Harold U. Escobar-Hernandez et al. INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH
- FAIR data enabling new horizons for materials research
- (2022) Matthias Scheffler et al. NATURE
- MOFSimplify, machine learning models with extracted stability data of three thousand metal–organic frameworks
- (2022) Aditya Nandy et al. Scientific Data
- Combining Machine Learning and Molecular Simulations to Unlock Gas Separation Potentials of MOF Membranes and MOF/Polymer MMMs
- (2022) Hilal Daglar et al. ACS Applied Materials & Interfaces
- Active learning boosted computational discovery of covalent–organic frameworks for ultrahigh CH 4 storage
- (2022) Hongjian Tang et al. AICHE JOURNAL
- A neural recommender system for efficient adsorbent screening
- (2022) Xiang Zhang et al. CHEMICAL ENGINEERING SCIENCE
- Using genetic algorithms to systematically improve the synthesis conditions of Al-PMOF
- (2022) Nency P. Domingues et al. Communications Chemistry
- Digital navigation of energy–structure–function maps for hydrogen-bonded porous molecular crystals
- (2021) Chengxi Zhao et al. Nature Communications
- Computational Screening of Trillions of Metal–Organic Frameworks for High-Performance Methane Storage
- (2021) Sangwon Lee et al. ACS Applied Materials & Interfaces
- Fast and Accurate Machine Learning Strategy for Calculating Partial Atomic Charges in Metal–Organic Frameworks
- (2021) Srinivasu Kancharlapalli et al. Journal of Chemical Theory and Computation
- A Universal Standard Archive File for Adsorption Data
- (2021) Jack D. Evans et al. LANGMUIR
- Machine learning and descriptor selection for the computational discovery of metal-organic frameworks
- (2021) Krishnendu Mukherjee et al. MOLECULAR SIMULATION
- Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks
- (2021) Aditi S. Krishnapriyan et al. Scientific Reports
- Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials
- (2021) Arni Sturluson et al. CHEMISTRY OF MATERIALS
- Using collective knowledge to assign oxidation states of metal cations in metal–organic frameworks
- (2021) Kevin Maik Jablonka et al. Nature Chemistry
- Fingerprinting diverse nanoporous materials for optimal hydrogen storage conditions using meta-learning
- (2021) Yangzesheng Sun et al. Science Advances
- Benchmarking graph neural networks for materials chemistry
- (2021) Victor Fung et al. npj Computational Materials
- Rapid Screening of Metal–Organic Frameworks for Propane/Propylene Separation by Synergizing Molecular Simulation and Machine Learning
- (2021) Hongjian Tang et al. ACS Applied Materials & Interfaces
- Diversifying Databases of Metal Organic Frameworks for High-Throughput Computational Screening
- (2021) Sauradeep Majumdar et al. ACS Applied Materials & Interfaces
- Accelerate Synthesis of Metal–Organic Frameworks by a Robotic Platform and Bayesian Optimization
- (2021) Yunchao Xie et al. ACS Applied Materials & Interfaces
- Machine‐Learning Prediction of Metal–Organic Framework Guest Accessibility from Linker and Metal Chemistry
- (2021) Rémi Pétuya et al. ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
- Using Machine Learning and Data Mining to Leverage Community Knowledge for the Engineering of Stable Metal–Organic Frameworks
- (2021) Aditya Nandy et al. JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
- Combining crystal graphs and domain knowledge in machine learning to predict metal-organic frameworks performance in methane adsorption
- (2021) Ruihan Wang et al. MICROPOROUS AND MESOPOROUS MATERIALS
- Machine Learning-Driven Insights into Defects of Zirconium Metal–Organic Frameworks for Enhanced Ethane–Ethylene Separation
- (2020) Ying Wu et al. CHEMISTRY OF MATERIALS
- Big-Data Science in Porous Materials: Materials Genomics and Machine Learning
- (2020) Kevin Maik Jablonka et al. CHEMICAL REVIEWS
- Message Passing Neural Networks for Partial Charge Assignment to Metal-Organic Frameworks
- (2020) Ali Raza et al. Journal of Physical Chemistry C
- Beyond the BET Analysis: The Surface Area Prediction of Nanoporous Materials Using a Machine Learning Method
- (2020) Archit Datar et al. Journal of Physical Chemistry Letters
- Understanding the diversity of the metal-organic framework ecosystem
- (2020) Seyed Mohamad Moosavi et al. Nature Communications
- Transferable and Extensible Machine Learning-Derived Atomic Charges for Modeling Hybrid Nanoporous Materials
- (2020) Vadim V. Korolev et al. CHEMISTRY OF MATERIALS
- Materials Cloud, a platform for open computational science
- (2020) Leopold Talirz et al. Scientific Data
- Materials Informatics with PoreBlazer v4.0 and the CSD MOF Database
- (2020) Lev Sarkisov et al. CHEMISTRY OF MATERIALS
- Machine Learning-Aided Computational Study of Metal–Organic Frameworks for Sour Gas Sweetening
- (2020) Eun Hyun Cho et al. Journal of Physical Chemistry C
- Capturing chemical intuition in synthesis of metal-organic frameworks
- (2019) Seyed Mohamad Moosavi et al. Nature Communications
- From Molecular Fragments to the Bulk: Development of a Neural Network Potential for MOF-5
- (2019) Marco Eckhoff et al. Journal of Chemical Theory and Computation
- A Robust Machine Learning Algorithm for the Prediction of Methane Adsorption in Nanoporous Materials
- (2019) George S. Fanourgakis et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Unsupervised word embeddings capture latent knowledge from materials science literature
- (2019) Vahe Tshitoyan et al. NATURE
- Identification Schemes for Metal–Organic Frameworks To Enable Rapid Search and Cheminformatics Analysis
- (2019) Benjamin J. Bucior et al. CRYSTAL GROWTH & DESIGN
- Advances, Updates, and Analytics for the Computation-Ready, Experimental Metal–Organic Framework Database: CoRE MOF 2019
- (2019) Yongchul G. Chung et al. JOURNAL OF CHEMICAL AND ENGINEERING DATA
- Data-driven design of metal–organic frameworks for wet flue gas CO2 capture
- (2019) Peter G. Boyd et al. NATURE
- High-throughput screening approach for nanoporous materials genome using topological data analysis: application to zeolites
- (2018) Yongjin Lee et al. Journal of Chemical Theory and Computation
- Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties
- (2018) Tian Xie et al. PHYSICAL REVIEW LETTERS
- Role of Pore Chemistry and Topology in the CO2 Capture Capabilities of MOFs: From Molecular Simulation to Machine Learning
- (2018) Ryther Anderson et al. CHEMISTRY OF MATERIALS
- Metallic Metal–Organic Frameworks Predicted by the Combination of Machine Learning Methods and Ab Initio Calculations
- (2018) Yuping He et al. Journal of Physical Chemistry Letters
- Dimensionality reduction for visualizing single-cell data using UMAP
- (2018) Etienne Becht et al. NATURE BIOTECHNOLOGY
- Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships
- (2017) Jon Paul Janet et al. JOURNAL OF PHYSICAL CHEMISTRY A
- Force-Field Prediction of Materials Properties in Metal-Organic Frameworks
- (2017) Peter G. Boyd et al. Journal of Physical Chemistry Letters
- Quantifying similarity of pore-geometry in nanoporous materials
- (2017) Yongjin Lee et al. Nature Communications
- Machine Learning Using Combined Structural and Chemical Descriptors for Prediction of Methane Adsorption Performance of Metal Organic Frameworks (MOFs)
- (2017) Maryam Pardakhti et al. ACS Combinatorial Science
- molSimplify: A toolkit for automating discovery in inorganic chemistry
- (2016) Efthymios I. Ioannidis et al. JOURNAL OF COMPUTATIONAL CHEMISTRY
- Geometrical Properties Can Predict CO2 and N2 Adsorption Performance of Metal–Organic Frameworks (MOFs) at Low Pressure
- (2016) Michael Fernandez et al. ACS Combinatorial Science
- RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials
- (2015) David Dubbeldam et al. MOLECULAR SIMULATION
- Machine learning: Trends, perspectives, and prospects
- (2015) M. I. Jordan et al. SCIENCE
- High-throughput computational screening of metal–organic frameworks
- (2014) Yamil J. Colón et al. CHEMICAL SOCIETY REVIEWS
- Computation-Ready, Experimental Metal–Organic Frameworks: A Tool To Enable High-Throughput Screening of Nanoporous Crystals
- (2014) Yongchul G. Chung et al. CHEMISTRY OF MATERIALS
- Atomic Property Weighted Radial Distribution Functions Descriptors of Metal–Organic Frameworks for the Prediction of Gas Uptake Capacity
- (2013) Michael Fernandez et al. Journal of Physical Chemistry C
- The Chemistry and Applications of Metal-Organic Frameworks
- (2013) H. Furukawa et al. SCIENCE
- Commentary: The Materials Project: A materials genome approach to accelerating materials innovation
- (2013) Anubhav Jain et al. APL Materials
- Algorithms and tools for high-throughput geometry-based analysis of crystalline porous materials
- (2011) Thomas F. Willems et al. MICROPOROUS AND MESOPOROUS MATERIALS
- Large-scale screening of hypothetical metal–organic frameworks
- (2011) Christopher E. Wilmer et al. Nature Chemistry
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