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The role of computational results databases in accelerating the discovery of catalysts

Nature Catalysis volume 1, pages 809–810 (2018)Cite this article

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Databases of computational results hold high promise for accelerating catalysis research. Still, many challenges remain and consensus on facets such as metadata, reliability and curation is crucial to transform the hype into an attractive technology.

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References

  1. NIST Chemistry WebBook. NIST Standard Reference Database Number 69 (NIST, accessed 26 May 2018); https://webbook.nist.gov/chemistry

  2. Protein Data Bank (RCSB, accessed 26 May 2018); https://www.rcsb.org

  3. The Cambridge Structural Database (CCDC, accessed 26 May 2018); https://www.ccdc.cam.ac.uk

  4. Inorganic Crystal Structure Database (FIZ Karlsruhe, accessed 26 May 2018); http://www2.fiz-karlsruhe.de/icsd_home.html

  5. Lejaeghere, K. et al. Science 351, aad3000 (2016).

    Article  Google Scholar 

  6. Ohno, K. & Morokuma, K. Quantum Chemistry Literature Data Base—Bibliography of Ab Initio Calculations for 1978–1980 (Elsevier, Amsterdam, 1982).

    Google Scholar 

  7. QCLDB II (QCDB Group, accessed 25 May 2018); http://qcldb2.ims.ac.jp

  8. Computational Chemistry Comparison and Benchmark Database, NIST Standard Reference Database Number 101 Release 19 (NIST, accessed 26 May 2018); https://cccbdb.nist.gov

  9. Hobza, P. Benchmark Energy and Geometry Database (Institute of Organic Chemistry and Biochemistry, Prague, accessed 26 May 2018); http://www.begdb.com

  10. Databases Truhlar Research Group (accessed 26 May 2018); http://truhlar.chem.umn.edu/content/databases

  11. Ghahremanpour, M. M., van Maaren, P. J. & van der Spoel, D. Sci. Data 5, 180062 (2018).

    Article  CAS  Google Scholar 

  12. Nakata, M. & Shimazaki, T. J. Chem. Inf. Model 57, 1300–1308 (2017).

    Article  CAS  Google Scholar 

  13. Open Babel: The Open Source Chemistry Toolbox (accessed 26 May 2018); http://openbabel.org/wiki/Main_Page

  14. Murray-Rust, P. & Rzepa, H. S. J. Cheminformatics 3, 44 (2011).

    Article  Google Scholar 

  15. Adams, S. et al. J. Cheminformatics 3, 38 (2011).

    Article  Google Scholar 

  16. Hummelshøj, J. S., Abild-Pedersen, F., Studt, F., Bligaard, T. & Nørskov, J. K. Angew. Chem. Int. Ed. 51, 272–274 (2011).

    Article  Google Scholar 

  17. Laloo, J. Z. A., Laloo, N., Rhyman, L. & Ramassami, P. J. Comput. Aided Mol. Des. 31, 667–673 (2017).

    Article  CAS  Google Scholar 

  18. Rodríguez-Guerra Pedregal, J., Gómez-Orellana, P. & Maréchal, J.-D. J. Chem. Inf. Model. 58, 561–564 (2018).

    Article  Google Scholar 

  19. O’Boyle, N. M., Tenderholt, A. L. & Langner, K. M. J. Comput. Chem. 29, 839–845 (2008).

    Article  Google Scholar 

  20. Materials Genome Initiative (accessed 29 May 2018); https://mgi.gov

  21. The Materials Project (accessed 30 August 2018); https://www.materialsproject.org

  22. Tabor, D. P. et al. Nat. Rev. Mater. 3, 5 (2018).

    Article  CAS  Google Scholar 

  23. The European Materials Modelling Council (accessed 30 August 2018); https://emmc.info

  24. de Bass, A. F. What Makes a Material Function (EU, 2017)

  25. NOMAD Repository (NOMAD Laboratory, accessed 23 May 2018); https://nomad-repository.eu

  26. Automated Interactive Infrastructure and Database for Computational Science (accessed 25 May 2018); http://www.aiida.net

  27. Pizzi, G., Cepellotti, A., Sabatini, R., Marzari, N. & Kozinsky, B. Comput. Mater. Sci. 111, 218–230 (2016).

    Article  Google Scholar 

  28. Web platform “Materials Cloud” could help industry streamline research efforts. Marvel http://nccr-marvel.ch/highlights/2018-05-web-platform-materials-cloud-could-help-industry (30 May 2018).

  29. Computational Materials Repository (CAMd, accessed 14 September 2018); https://cmr.fysik.dtu.dk

  30. Álvarez-Moreno, M. et al. J. Chem. Inf. Model. 55, 95 (2015).

    Article  Google Scholar 

  31. ioChem-BD (accessed 29 May 2018); http://www.iochem-bd.org

  32. Chen, Z. Nat. Nanotech 13, 702–707 (2018).

    Article  CAS  Google Scholar 

  33. Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O. & Walsh, A. Nature 559, 547–555 (2018).

    Article  CAS  Google Scholar 

  34. Wang, B., Dobosh, P. A., Chalk, S., Sopek, M. & Ostlund, N. S. J. Phys. Chem. A 121, 298–307 (2016).

    Article  Google Scholar 

  35. Rossi, E. et al. J. Comput. Chem. 35, 611–621 (2014).

    Article  CAS  Google Scholar 

  36. Ghiringhelli, L. M. npj Comput. Mater. 3, 46 (2017).

    Article  Google Scholar 

  37. The Molecular Sciences Software Institute (accessed 30 August 2018); https://molssi.org

  38. Schütt, K. T., Arbabzadah, F., Chmiela, S. & Müller, K.-R. Nat. Commun. 8, 13890 (2017).

    Article  Google Scholar 

  39. Janet, J. P. & Kulik, H. J. Chem. Sci. 8, 5137–5152 (2017).

    Article  CAS  Google Scholar 

  40. Ferguson, A. L. ACS Cent. Sci. 4, 938–941 (2018).

    Article  CAS  Google Scholar 

  41. Gómez-Bombarelli, R. et al. ACS Cent. Sci. 4, 268–276 (2018).

    Article  Google Scholar 

  42. Nandy, A., Duan, C., Janet, J. P., Gugler, S. & Kulik, H. Preprint at https://doi.org/10.26434/chemrxiv.6987074.v1 (2018).

  43. Jones, G. Nat. Catal. 1, 311–313 (2018).

    Article  Google Scholar 

  44. Wu, Z. et al. Chem. Sci. 9, 513–530 (2018).

    Article  CAS  Google Scholar 

  45. Lemonick, S. Is machine learning overhyped? Chem. Eng. News https://cen.acs.org/physical-chemistry/computational-chemistry/machine-learning-overhyped/96/i34 (2018).

  46. PASC18 panel discussion. Is HPC facing a game change? YouTube https://www.youtube.com/watch?v=mTqzCvm0G5c (16 July 2018).

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Authors and Affiliations

  1. Institute of Chemical Research of Catalonia, The Barcelona Institute of Science and Technology, Tarragona, Spain

    Carles Bo, Feliu Maseras & Núria López

  2. Universitat Rovira i Virgili, Departament de Química Física i Inorgànica, Tarragona, Spain

    Carles Bo

  3. Universitat Autònoma de Barcelona, Departament de Química, Bellaterra, Spain

    Feliu Maseras

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  1. Carles Bo
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  2. Feliu Maseras
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  3. Núria López
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Correspondence to Núria López.

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Bo, C., Maseras, F. & López, N. The role of computational results databases in accelerating the discovery of catalysts. Nat Catal 1, 809–810 (2018). https://doi.org/10.1038/s41929-018-0176-4

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