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Modeling, Optimization, and Machine ...

Pham, Thang Duc,

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Modeling, Optimization, and Machine Learning Insights for Room Temperature H₂ Storage and CO₂ Capture in Metal-Organic Frameworks /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Modeling, Optimization, and Machine Learning Insights for Room Temperature H₂ Storage and CO₂ Capture in Metal-Organic Frameworks // Thang Duc Pham.
作者:	Pham, Thang Duc,
面頁冊數:	1 electronic resource (192 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-02, Section: B.
Contained By:	Dissertations Abstracts International86-02B.
標題:	Chemical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31329252
ISBN:	9798384015505

Modeling, Optimization, and Machine Learning Insights for Room Temperature H₂ Storage and CO₂ Capture in Metal-Organic Frameworks /
Pham, Thang Duc,

Modeling, Optimization, and Machine Learning Insights for Room Temperature H₂ Storage and CO₂ Capture in Metal-Organic Frameworks /Thang Duc Pham. - 1 electronic resource (192 pages)

Source: Dissertations Abstracts International, Volume: 86-02, Section: B.

Metal-organic frameworks (MOFs), a class of porous crystalline materials consisting of metal nodes and organic linkers, are promising for various applications such as energy storage, gas separation and catalysis. This study aimed to employ molecular modeling, optimization techniques and machine learning to accelerate the process of identifying suitable MOF candidates for CO2 capture and room temperature H2 storage. First, we developed two optimization methods, namely genetic algorithms (Chapter 2) and a combination of latent variable Gaussian process and Bayesian optimization (LVGP-BO) (Chapter 3), to optimize MOFs for CO2 capture. Both methods utilized molecular simulations to evaluate the objective functions and identify MOFs with high CO2 working capacity and selectivity. These optimization methods not only accelerate the search for top-performing MOFs for CO2 capture and reduce the number of molecular simulations required, but also help understand the structure-property relationships in MOFs for CO2 capture.Second, we developed machine learning (ML) models to predict partial atomic charges in both neutral and ionic MOFs (Chapter 4). The ML models can accurately predict density function theory (DFT) charges at a fraction of DFT cost. The model is transferable to other porous materials, such as covalent organic frameworks and zeolites. The models show a significant improvement versus previous models, especially for simulations involving water.Third, we investigated room temperature H2 storage in anionic MOFs with extra-framework cations (Chapter 5) and in MOFs with metal-paddlewheel secondary building units (SBUs) (Chapter 6). For the anionic MOFs, our DFT calculations show that Mg2+ ions exhibit the strongest interaction with H2 and are promising for this application. For MOFs with metal-paddlewheel SBUs, our DFT models show a strong agreement with experimental measurements for Rh and Ru paddlewheels. Through computational screening, we further identified other metal paddlewheels that have potential for room temperature H2 storage, such as Ir, Os and Ni. Overall, the dissertation demonstrates how molecular modeling, optimization and machine learning can be used together to accelerate the design of MOFs for CO2 capture, H2 storage at room temperature and other adsorption-based applications.

English

ISBN: 9798384015505Subjects--Topical Terms:

560457
Chemical engineering.
Subjects--Index Terms:

Adsorption

Modeling, Optimization, and Machine Learning Insights for Room Temperature H₂ Storage and CO₂ Capture in Metal-Organic Frameworks /
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300 $a 1 electronic resource (192 pages)
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500 $a Advisors: Snurr, Randall Q. Committee members: Farha, Omar K.; Chen, Wei; Amaral, Luis.
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520 $a Metal-organic frameworks (MOFs), a class of porous crystalline materials consisting of metal nodes and organic linkers, are promising for various applications such as energy storage, gas separation and catalysis. This study aimed to employ molecular modeling, optimization techniques and machine learning to accelerate the process of identifying suitable MOF candidates for CO2 capture and room temperature H2 storage. First, we developed two optimization methods, namely genetic algorithms (Chapter 2) and a combination of latent variable Gaussian process and Bayesian optimization (LVGP-BO) (Chapter 3), to optimize MOFs for CO2 capture. Both methods utilized molecular simulations to evaluate the objective functions and identify MOFs with high CO2 working capacity and selectivity. These optimization methods not only accelerate the search for top-performing MOFs for CO2 capture and reduce the number of molecular simulations required, but also help understand the structure-property relationships in MOFs for CO2 capture.Second, we developed machine learning (ML) models to predict partial atomic charges in both neutral and ionic MOFs (Chapter 4). The ML models can accurately predict density function theory (DFT) charges at a fraction of DFT cost. The model is transferable to other porous materials, such as covalent organic frameworks and zeolites. The models show a significant improvement versus previous models, especially for simulations involving water.Third, we investigated room temperature H2 storage in anionic MOFs with extra-framework cations (Chapter 5) and in MOFs with metal-paddlewheel secondary building units (SBUs) (Chapter 6). For the anionic MOFs, our DFT calculations show that Mg2+ ions exhibit the strongest interaction with H2 and are promising for this application. For MOFs with metal-paddlewheel SBUs, our DFT models show a strong agreement with experimental measurements for Rh and Ru paddlewheels. Through computational screening, we further identified other metal paddlewheels that have potential for room temperature H2 storage, such as Ir, Os and Ni. Overall, the dissertation demonstrates how molecular modeling, optimization and machine learning can be used together to accelerate the design of MOFs for CO2 capture, H2 storage at room temperature and other adsorption-based applications.
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650 4 $a Chemical engineering. $3 560457
650 4 $a Computational chemistry. $3 3350019
650 4 $a Materials science. $3 543314
653 $a Adsorption
653 $a Machine learning
653 $a Metal-organic frameworks
653 $a Molecular modeling
653 $a Optimization
653 $a Separation
690 $a 0542
690 $a 0219
690 $a 0794
710 2 $a Northwestern University. $b Chemical and Biological Engineering. $e degree granting institution. $3 3816421
720 1 $a Snurr, Randall Q. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-02B.
790 $a 0163
791 $a Ph.D.
792 $a 2024
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