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Turning chemistry into information for heterogeneous catalysis
  • Sergio Pablo-GarcíaOrcid,
  • Moises Álvarez-Moreno,
  • Núria López
Sergio Pablo-García
Orcid
The Barcelona Institute of Science and Technology, BIST, Institute of Chemical Research of Catalonia, ICIQ, Av. Països Catalans 16, 43007 Tarragona, Catalonia, Spain
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Moises Álvarez-Moreno
Institute of Chemical Research of Catalonia, ICIQ, Av. Països Catalans 16, 43007 Tarragona, Catalonia, Spain, Department of Physical and Inorganic Chemistry, Universitat Rovira i Virgili, C/Marcel·lí Domingo s/n, 43007 Tarragona, Catalonia, Spain
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Núria López
The Barcelona Institute of Science and Technology, BIST, Institute of Chemical Research of Catalonia, ICIQ, Av. Països Catalans 16, 43007 Tarragona, Catalonia, Spain
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Peer review status:Published

02 Oct 2019Submitted to IJQC Special Issue
08 Oct 2019Reviewer(s) Assigned
16 Dec 2019Review(s) Completed, Editorial Evaluation Pending
19 May 20201st Revision Received
11 Jun 2020Editorial Decision: Accept
Published in 10.1002/qua.26382

Abstract

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 and 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 full automated procedure consisting in 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 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.