Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal–Organic Frameworks
出版年份 2022 全文链接
标题
Interpretable Graph Transformer Network for Predicting Adsorption Isotherms of Metal–Organic Frameworks
作者
关键词
-
出版物
Journal of Chemical Information and Modeling
Volume -, Issue -, Pages -
出版商
American Chemical Society (ACS)
发表日期
2022-11-02
DOI
10.1021/acs.jcim.2c00876
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- (2021) Rishi Gurnani et al. CHEMISTRY OF MATERIALS
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- Recommendation System to Predict Missing Adsorption Properties of Nanoporous Materials
- (2021) Arni Sturluson et al. CHEMISTRY OF MATERIALS
- Highly accurate protein structure prediction with AlphaFold
- (2021) John Jumper et al. NATURE
- Accurate prediction of protein structures and interactions using a three-track neural network
- (2021) Minkyung Baek et al. SCIENCE
- Extracting Predictive Representations from Hundreds of Millions of Molecules
- (2021) Dong Chen et al. Journal of Physical Chemistry Letters
- Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model
- (2020) Ryther Anderson et al. Journal of Chemical Theory and Computation
- Computational Design of Photo-Responsive Metal-Organic Framework for Post Combustion Carbon Capture
- (2020) Junkil Park et al. Journal of Physical Chemistry C
- Transfer Learning Study of Gas Adsorption in Metal–Organic Frameworks
- (2020) Ruimin Ma et al. ACS Applied Materials & Interfaces
- High-Performing Deep Learning Regression Models for Predicting Low-Pressure CO2 Adsorption Properties of Metal–Organic Frameworks
- (2020) Jake Burner et al. Journal of Physical Chemistry C
- Stable Amide-Functionalized Metal–Organic Framework with Highly Selective CO2 Adsorption
- (2019) Cong Chen et al. INORGANIC CHEMISTRY
- 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
- Convolutional Neural Network of Atomic Surface Structures To Predict Binding Energies for High-Throughput Screening of Catalysts
- (2019) Seoin Back et al. Journal of Physical Chemistry Letters
- Intermediate-sized molecular sieving of styrene from larger and smaller analogues
- (2019) Dong-Dong Zhou et al. NATURE MATERIALS
- Data-driven design of metal–organic frameworks for wet flue gas CO2 capture
- (2019) Peter G. Boyd et al. NATURE
- Catalysis and photocatalysis by metal organic frameworks
- (2018) Amarajothi Dhakshinamoorthy et al. CHEMICAL SOCIETY REVIEWS
- DeePMD-kit: A deep learning package for many-body potential energy representation and molecular dynamics
- (2018) Han Wang et al. COMPUTER PHYSICS COMMUNICATIONS
- SchNet – A deep learning architecture for molecules and materials
- (2018) K. T. Schütt et al. JOURNAL OF CHEMICAL PHYSICS
- 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
- Attainable Volumetric Targets for Adsorption-Based Hydrogen Storage in Porous Crystals: Molecular Simulation and Machine Learning
- (2018) Grace Anderson et al. Journal of Physical Chemistry C
- Computational Study of Water Adsorption in the Hydrophobic Metal–Organic Framework ZIF-8: Adsorption Mechanism and Acceleration of the Simulations
- (2017) Hongda Zhang et al. Journal of Physical Chemistry C
- 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
- Machine Learning Approach for Prediction and Search: Application to Methane Storage in a Metal–Organic Framework
- (2016) Hiroshi Ohno et al. Journal of Physical Chemistry C
- RASPA: molecular simulation software for adsorption and diffusion in flexible nanoporous materials
- (2015) David Dubbeldam et al. MOLECULAR SIMULATION
- Physisorption of gases, with special reference to the evaluation of surface area and pore size distribution (IUPAC Technical Report)
- (2015) Matthias Thommes et al. PURE AND APPLIED CHEMISTRY
- Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture
- (2014) Michael Fernandez et al. Journal of Physical Chemistry Letters
- Porous Metal–Organic Frameworks for Gas Storage and Separation: What, How, and Why?
- (2014) Bin Li et al. Journal of Physical Chemistry Letters
- Theoretical Limits of Hydrogen Storage in Metal–Organic Frameworks: Opportunities and Trade-Offs
- (2013) Jacob Goldsmith et al. CHEMISTRY OF MATERIALS
- Evaluating metal–organic frameworks for natural gas storage
- (2013) Jarad A. Mason et al. Chemical Science
- 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
- Towards rapid computational screening of metal-organic frameworks for carbon dioxide capture: Calculation of framework charges via charge equilibration
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- (2008) Jagadeswara R. Karra et al. LANGMUIR
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