Molecular Fingerprint and Machine Learning to Accelerate Design of High‐Performance Homochiral Metal‐Organic Frameworks

AIChE Journal ◽  
2021 ◽  
Author(s):  
Zhiwei Qiao ◽  
Lifeng Li ◽  
Shuhua Li ◽  
Hong Liang ◽  
Jian Zhou ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Metal Organic Frameworks ◽  
Molecular Fingerprint ◽  
Metal Organic

scholarly journals Machine Learning-Assisted Computational Screening of Metal-Organic Frameworks for Atmospheric Water Harvesting

Nanomaterials ◽  
2022 ◽  
Vol 12 (1) ◽  
pp. 159
Author(s):  
Lifeng Li ◽  
Zenan Shi ◽  
Hong Liang ◽  
Jie Liu ◽  
Zhiwei Qiao
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Univariate Analysis ◽  
Metal Organic Frameworks ◽  
Boosted Regression Trees ◽  
Water Harvesting ◽  
Atmospheric Water ◽  
Computational Screening ◽  
Metal Organic ◽  
And Performance

Atmospheric water harvesting by strong adsorbents is a feasible method of solving the shortage of water resources, especially for arid regions. In this study, a machine learning (ML)-assisted high-throughput computational screening is employed to calculate the capture of H2O from N2 and O2 for 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) and 137,953 hypothetical MOFs (hMOFs). Through the univariate analysis of MOF structure-performance relationships, Qst is shown to be a key descriptor. Moreover, three ML algorithms (random forest, gradient boosted regression trees, and neighbor component analysis (NCA)) are applied to hunt for the complicated interrelation between six descriptors and performance. After the optimizing strategy of grid search and five-fold cross-validation is performed, three ML can effectively build the predictive model for CoRE-MOFs, and the accuracy R2 of NCA can reach 0.97. In addition, based on the relative importance of the descriptors by ML, it can be quantitatively concluded that the Qst is dominant in governing the capture of H2O. Besides, the NCA model trained by 6013 CoRE-MOFs can predict the selectivity of hMOFs with a R2 of 0.86, which is more universal than other models. Finally, 10 CoRE-MOFs and 10 hMOFs with high performance are identified. The computational screening and prediction of ML could provide guidance and inspiration for the development of materials for water harvesting in the atmosphere.

Machine-learning-assisted high-throughput computational screening of high performance metal–organic frameworks

2020 ◽  
Vol 5 (4) ◽  
pp. 725-742 ◽  
Author(s):  
Zenan Shi ◽  
Wenyuan Yang ◽  
Xiaomei Deng ◽  
Chengzhi Cai ◽  
Yaling Yan ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
High Performance ◽  
Metal Organic Frameworks ◽  
Computational Screening ◽  
Metal Organic

The combination of machine learning and high-throughput computation for the screening of MOFs with high performance.

Techno-Economic Analysis of Metal–Organic Frameworks for Adsorption Heat Pumps/Chillers: From Directional Computational Screening, Machine Learning to Experiment

2021 ◽  
Author(s):  
Zenan Shi ◽  
Xueying Yuan ◽  
Yaling Yan ◽  
Yuanlin Tang ◽  
Junjie Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Economic Analysis ◽  
High Performance ◽  
Metal Organic Frameworks ◽  
Heat Pumps ◽  
Adsorption Heat ◽  
Organic P ◽  
Computational Screening ◽  
Metal Organic ◽  
Low Costs

The key to achieving high efficiencies, high performance, and low costs of adsorption heat pumps/chillers (AHPs/ACs) is to choose a suitable adsorbent. A computational screening of 6,013 computation-ready experimental metal–organic...

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

Rational strategies for proton-conductive metal–organic frameworks

2021 ◽  
Author(s):  
Dae-Woon Lim ◽  
Hiroshi Kitagawa
Keyword(s):  
Solid State ◽  
Potential Application ◽  
High Performance ◽  
Metal Organic Frameworks ◽  
Metal Organic ◽  
Solid State Electrolytes

Since the transition of energy platforms, the proton-conductive metal–organic frameworks (MOFs) exhibiting high performance have been extensively investigated with rational strategies for their potential application in solid-state electrolytes.

Sign in / Sign up

Export Citation Format

Share Document