The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Managing the Computational Chemistry Big Data Problem: The ioChem-BD Platform
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Quantum chemistry structures and properties of 134 kilo molecules
Ramakrishnan, R., Dral, P. O., Rupp, M., & von Lilienfeld, O. A. (2014) · Scientific Data · doi:10.1038/sdata.2014.22 · Cited by 1,966
Cited by
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
Self-Driving Laboratories for Chemistry and Materials Science
Tom, G., Schmid, S. P., Baird, S. G., Cao, Y., Darvish, K., Hao, H., Lo, S., Pablo-García, S., Rajaonson, E. M., Skreta, M., et al. (2024) · Chemical Reviews · doi:10.1021/acs.chemrev.4c00055 · Cited by 707
Zhan, C., Dattila, F., Rettenmaier, C., Bergmann, A., Kühl, S., García-Muelas, R., López, N., & Cuenya, B. R. (2021) · ACS Catalysis · doi:10.1021/acscatal.1c01478 · Cited by 554
Self-citationShih, A. J., Monteiro, M. C. O., Dattila, F., Pavesi, D., Philips, M., da Silva, A. H. M., Vos, R. E., Ojha, K., Park, S., van der Heijden, O., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00164-0 · Cited by 521
Self-citationFrei, M. S., Mondelli, C., García-Muelas, R., Kley, K. S., Puértolas, B., López, N., Safonova, O. V., Stewart, J. A., Curulla Ferré, D., & Pérez-Ramírez, J. (2019) · Nature Communications · doi:10.1038/s41467-019-11349-9 · Cited by 469
Self-citationDynamic charge and oxidation state of Pt/CeO2 single-atom catalysts
Daelman, N., Capdevila-Cortada, M., & López, N. (2019) · Nature Materials · doi:10.1038/s41563-019-0444-y · Cited by 435
Self-citationCatalysis-Hub.org, an open electronic structure database for surface reactions
Winther, K. T., Hoffmann, M. J., Boes, J. R., Mamun, O., Bajdich, M., & Bligaard, T. (2019) · Scientific Data · doi:10.1038/s41597-019-0081-y · Cited by 313
Bridging the complexity gap in computational heterogeneous catalysis with machine learning
Mou, T., Pillai, H. S., Wang, S., Wan, M., Han, X., Schweitzer, N. M., Che, F., & Xin, H. (2023) · Nature Catalysis · doi:10.1038/s41929-023-00911-w · Cited by 280
Saadun, A. J., Pablo-García, S., Paunović, V., Li, Q., Sabadell-Rendón, A., Kleemann, K., Krumeich, F., López, N., & Pérez-Ramírez, J. (2020) · ACS Catalysis · doi:10.1021/acscatal.0c00679 · Cited by 30
Self-citationCurti, M., Maffeis, V., Teixeira Alves Duarte, L. G., Shareef, S., Hallado, L. X., Curutchet, C., & Romero, E. (2023) · Protein Science · doi:10.1002/pro.4579 · Cited by 10
Metadata
- Authors
- Álvarez-Moreno, M., de Graaf, C., López, N., Maseras, F., Poblet, J. M., & Bo, C.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2014-12-15
- Volume
- 55
- Issue
- 1
- Pages
- 95-103
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2053014304
- Cited by
- 646
- References
- 1
- FWCI
- 33.05
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
