We present the ioChem-BD platform ( www.iochem-bd.org ) as a multiheaded tool aimed to manage large volumes of quantum chemistry results from a diverse group of already common simulation packages. The platform has an extensible structure. The key modules managing the main tasks are to (i) upload of output files from common computational chemistry packages, (ii) extract meaningful data from the results, and (iii) generate output summaries in user-friendly formats. A heavy use of the Chemical Mark-up Language (CML) is made in the intermediate files used by ioChem-BD. From them and using XSL techniques, we manipulate and transform such chemical data sets to fulfill researchers' needs in the form of HTML5 reports, supporting information, and other research media.
The level of detail attained in the computational description of reaction mechanisms can be vastly improved through tools for automated chemical space exploration, particularly for systems of small to medium size. Under this approach, the unimolecular decomposition landscape for indole was explored through the automated reaction mechanism discovery program AutoMeKin. Nevertheless, the sheer complexity of the obtained mechanisms might be a hindrance regarding their chemical interpretation. In this spirit, the new Python library amk-tools has been designed to read and manipulate complex reaction networks, greatly simplifying their overall analysis. The package provides interactive dashboards featuring visualizations of the network, the three-dimensional (3D) molecular structures and vibrational normal modes of all chemical species, and the corresponding energy profiles for selected pathways. The combination of the joined mechanism generation and postprocessing workflow with the rich chemistry of indole decomposition enabled us to find new details of the reaction (obtained at the CCSD(T)/aug-cc-pVTZ//M06-2X/MG3S level of theory) that were not reported before: (i) 16 pathways leading to the formation of HCN and NH3 (via amino radical); (ii) a barrierless reaction between methylene radical and phenyl isocyanide, which might be an operative mechanism under the conditions of the interstellar medium; and (iii) reaction channels leading to both hydrogen cyanide and hydrogen isocyanide, of potential astrochemical interest as the computed HNC/HCN ratios greatly exceed the calculated equilibrium value at very low temperatures. The reported reaction networks can be very valuable to supplement databases of kinetic data, which is of remarkable interest for pyrolysis and astrochemical studies.
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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