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

2020 ◽  
Vol 124 (50) ◽  
pp. 27580-27591
Author(s):  
Eun Hyun Cho ◽  
Xuepeng Deng ◽  
Changlong Zou ◽  
Li-Chiang Lin
Keyword(s):  
Machine Learning ◽  
Computational Study ◽  
Metal Organic Frameworks ◽  
Gas Sweetening ◽  
Metal Organic ◽  
Sour Gas

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

Matter ◽  
2021 ◽  
Author(s):  
Andrew S. Rosen ◽  
Shaelyn M. Iyer ◽  
Debmalya Ray ◽  
Zhenpeng Yao ◽  
Alán Aspuru-Guzik ◽  
...  
Keyword(s):  
Machine Learning ◽  
Quantum Chemical ◽  
Metal Organic Frameworks ◽  
Chemical Properties ◽  
Metal Organic

The effect of the aliphatic carboxylate linkers on the electronic structures, chemical bonding and optical properties of the uranium-based metal–organic frameworks

RSC Advances ◽  
2015 ◽  
Vol 5 (34) ◽  
pp. 26735-26748 ◽  
Author(s):  
Saumitra Saha ◽  
Udo Becker
Keyword(s):  
Optical Properties ◽  
Detailed Analysis ◽  
Computational Methods ◽  
Chemical Bonding ◽  
Electronic Structures ◽  
Computational Study ◽  
Metal Organic Frameworks ◽  
Metal Organic ◽  
First Time

A series of uranyl containing aliphatic dicarboxylate structures is studied using computational methods. Our computational study provides a detailed analysis of these MOFs and explores the effect of linkers on their properties for the first time.

An Automated Machine Learning architecture for the accelerated prediction of Metal-Organic Frameworks performance in energy and environmental applications

2020 ◽  
Vol 300 ◽  
pp. 110160 ◽  
Author(s):  
Ioannis Tsamardinos ◽  
George S. Fanourgakis ◽  
Elissavet Greasidou ◽  
Emmanuel Klontzas ◽  
Konstantinos Gkagkas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Metal Organic Frameworks ◽  
Environmental Applications ◽  
Metal Organic ◽  
Automated Machine Learning
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