Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform

By M. Álvarez-Moreno, C. Graaf, N. López, F. Maseras, J. Poblet, C. Bo. Journal of Chemical Information and Modeling, 2015

505 citations recorded by the source corpus.

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.

Subfields: Computational Theory and Mathematics, Information Systems and Management, Materials Chemistry

View Paper: https://www.semanticscholar.org/paper/64c5532ae4c9041bc1acaf0d8e28a1b061224a4f

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In Computational Theory and Mathematics

In Materials Chemistry

Citations in the corpus

Atomate: A high-level interface to generate, execute, and analyze computational materials science workflows (2017, methodology): “Such efforts include commercial offerings such as Medea [9], Materials Studio [10], and GoVasp (now part of Medea) and academic codes such as AiiDA [11], MAST [12], qmpy/OQMD [13], ASE [14], AFLOW [15], the Harvard Clean Energy Project [16], iochem-bd [17], Quixote [18], and our own previous efforts”