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Combining data science, computer science, and statistical mechanics for the discovery of Metal-Organic Frameworks

Description:

Metal-organic frameworks (MOFs) are porous, crystalline materials synthesized by combining metal nodes and organic linkers through self-assembly. The diverse range of building blocks available allows for extensive tunability of MOFs, enabling the optimization of these materials for various applications, such as gas storage, separations, and catalysis. This study aimed to...

Keyword:

Molecular modeling, Xylene Separation, Monte carlo simulation, Chemical engineering, and Metal-organic framework

Subject:

Chemistry and Chemical engineering

Creator:

Li, Zhao

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

08/23/2023

Date Created:

2023-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_1013711 and http://dissertations.umi.com/northwestern:16763
Data-Driven Methods to Accelerate the Design of Metal-Organic Frameworks

Description:

Metal-organic frameworks (MOFs) are a class of nanoporous materials with highly tunable pore shape and chemistry. They are synthesized in a "building block" approach to form crystalline porous materials, which have been explored for diverse applications including gas storage and separations. Given the enormous size of the MOF design space,...

Keyword:

Molecular modeling, Adsorption, Cheminformatics, Database, Data science, and Metal-organic framework

Subject:

Chemical engineering

Creator:

Bucior, Benjamin James

Owner:

Scholarly Digital Publishing

Language:

en

Date Uploaded:

02/12/2020

Date Created:

2019-01-01

Resource Type:

Dissertation

Alternate Identifier:

etdadmin_upload_679201 and http://dissertations.umi.com/northwestern:14767

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Combining data science, computer science, and statistical mechanics for the discovery of Metal-Organic Frameworks

Data-Driven Methods to Accelerate the Design of Metal-Organic Frameworks

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