CatMAP: A Software Package for Descriptor-Based Microkinetic Mapping of Catalytic Trends
Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters—reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem...
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| Published in | Catalysis letters Vol. 145; no. 3; pp. 794 - 807 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Boston
Springer US
01.03.2015
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1011-372X 1572-879X |
| DOI | 10.1007/s10562-015-1495-6 |
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| Abstract | Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters—reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few “descriptors” upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem—preferably to 1 or 2 descriptors—thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The “CatMAP” Python module seeks to standardize and automate many of the mathematical routines necessary to move from “descriptor space” to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A “reaction model” can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction.
Graphical Abstract |
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| AbstractList | Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters—reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few “descriptors” upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem—preferably to 1 or 2 descriptors—thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The “CatMAP” Python module seeks to standardize and automate many of the mathematical routines necessary to move from “descriptor space” to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A “reaction model” can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction.Graphical Abstract Descriptor-based analysis is a powerful tool for understanding the trends across various catalysts. In general, the rate of a reaction over a given catalyst is a function of many parameters—reaction energies, activation barriers, thermodynamic conditions, etc. The high dimensionality of this problem makes it very difficult and expensive to solve completely, and even a full solution would not give much insight into the rational design of new catalysts. The descriptor-based approach seeks to determine a few “descriptors” upon which the other parameters are dependent. By doing this it is possible to reduce the dimensionality of the problem—preferably to 1 or 2 descriptors—thus greatly reducing computational efforts and simultaneously increasing the understanding of trends in catalysis. The “CatMAP” Python module seeks to standardize and automate many of the mathematical routines necessary to move from “descriptor space” to reaction rates for heterogeneous (electro) catalysts. The module is designed to be both flexible and powerful, and is available for free online. A “reaction model” can be fully defined by a configuration file, thus no new programming is necessary to change the complexity or assumptions of a model. Furthermore, various steps in the process of moving from descriptors to reaction rates have been abstracted into separate Python classes, making it easy to change the methods used or add new functionality. This work discusses the structure of the code and presents the underlying algorithms and mathematical expressions both generally and via an example for the CO oxidation reaction. Graphical Abstract |
| Author | Bligaard, Thomas Lausche, Adam C. Shi, Chuan Hoffmann, Max J. Fitzgibbon, Sean R. Medford, Andrew J. Nørskov, Jens K. |
| Author_xml | – sequence: 1 givenname: Andrew J. surname: Medford fullname: Medford, Andrew J. email: ajmedfor@gmail.com organization: Department of Chemical Engineering, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 2 givenname: Chuan surname: Shi fullname: Shi, Chuan organization: Department of Chemical Engineering, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 3 givenname: Max J. surname: Hoffmann fullname: Hoffmann, Max J. organization: Department of Chemical Engineering, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 4 givenname: Adam C. surname: Lausche fullname: Lausche, Adam C. organization: Department of Chemical Engineering, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 5 givenname: Sean R. surname: Fitzgibbon fullname: Fitzgibbon, Sean R. organization: Department of Chemical Engineering, Stanford University – sequence: 6 givenname: Thomas surname: Bligaard fullname: Bligaard, Thomas organization: Department of Chemical Engineering, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis – sequence: 7 givenname: Jens K. surname: Nørskov fullname: Nørskov, Jens K. organization: Department of Chemical Engineering, Stanford University, SLAC National Accelerator Laboratory, SUNCAT Center for Interface Science and Catalysis |
| BackLink | https://www.osti.gov/biblio/1255571$$D View this record in Osti.gov |
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