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...

Large-scale evaluation of cascaded adsorption heat pumps based on metal/covalent–organic frameworks

2019 ◽  
Vol 7 (43) ◽  
pp. 25010-25019 ◽  
Author(s):  
Wei Li ◽  
Xiaoxiao Xia ◽  
Song Li
Keyword(s):  
High Throughput ◽  
Large Scale ◽  
Metal Organic Frameworks ◽  
Heat Pumps ◽  
Adsorption Heat ◽  
Covalent Organic Frameworks ◽  
Scale Evaluation ◽  
Computational Screening ◽  
Metal Organic

High-throughput computational screening of millions of cascaded adsorption heat pumps based on metal–organic frameworks and covalent–organic frameworks.

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.

scholarly journals Discovery of Record-Breaking Metal-Organic Frameworks for Methane Storage using Evolutionary Algorithm and Machine Learning

2020 ◽  
Author(s):  
Sangwon Lee ◽  
Baekjun Kim ◽  
Jihan Kim
Keyword(s):  
Machine Learning ◽  
Evolutionary Algorithm ◽  
Metal Organic Frameworks ◽  
Working Capacity ◽  
Computational Screening ◽  
Screening Approach ◽  
Systematic Strategy ◽  
Metal Organic ◽  
New Type ◽  
Modular Nature

In the past decade, there has been a rise in a number of computational screening works to facilitate finding optimal metal-organic frameworks (MOF) for variety of different applications. Unfortunately, most of these screening works are limited to its initial set of materials and result in brute-force type of a screening approach. In this work, we present a systematic strategy that can find materials with desired property from an extremely diverse and large MOF set of over 100 trillion possible MOFs using machine learning and evolutionary algorithm. It is demonstrated that our algorithm can discover 964 MOFs with methane working capacity over 200 cm<sup>3</sup> cm<sup>−3</sup> and 96 MOFs with methane working capacity over 208 cm<sup>3</sup> cm<sup>−3</sup>, which is the current world record. We believe that this methodology can facilitate a new type of a screening approach that takes advantage of the modular nature in MOFs, and can readily be extended to other important applications as well.

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