Turning chemistry into information for heterogeneous catalysis
The growing generation of data and their wide availability has led to the development of tools to produce, analyze, and store this information. Computational chemistry studies, especially catalytic applications, often yield a vast amount of chemical information that can be analyzed and stored using...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2020 |
| País: | España |
| Institución: | Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya) |
| Repositorio: | Recercat. Dipósit de la Recerca de Catalunya |
| OAI Identifier: | oai:recercat.cat:2072/450537 |
| Acceso en línea: | http://hdl.handle.net/2072/450537 https://doi.org/10.1002/qua.26382 |
| Access Level: | acceso abierto |
| Palabra clave: | 54 |
| Sumario: | The growing generation of data and their wide availability has led to the development of tools to produce, analyze, and store this information. Computational chemistry studies, especially catalytic applications, often yield a vast amount of chemical information that can be analyzed and stored using these tools. In this manuscript, we present a framework that automatically performs a fully automated procedure consisting of the transfer of an adsorbate from a known metal slab to a new metal slab with similar packing. Our method generates the new geometry and also performs the required calculations and analysis to finally upload the processed data to an online database (ioChem-BD). Two different implementations have been built, one to relocate minimum energy point structures and the second to transfer transition states. Our framework shows good performance for the minimum point location and a decent performance for the transition state identification. Most of the failures occurred during the transition state searches and needed additional steps to fully complete the process. Further improvements of our framework are required to increase the performance of both implementations. These results point to the avoidhuman path as a feasible solution for studies on very large systems that require a significant amount of human resources and, in consequence, are prone to human errors. |
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