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materials innovation”, APL Materials, 1, 11002 (2013); Materials Project, https://materialsproject. org; NIMS Materials Database (MatNavi), http://mits.nims.go.jp/index_en.html; C. Draxl and M. Scheffler, “NOMAD: The FAIR concept for big data-driven materials science”, MRS Bulletin, 43, 676–682 (2018); The Novel Materials Discovery (NOMAD) Laboratory, https://nomad-coe.eu; J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton, “Materials design and discovery with high- throughput density functional theory: the open quantum materials database (OQMD)”, JOM , 65, 1501– 1509 (2013); OQMD, http://oqmd.org; Open materials database, http://openmaterialsdb.se; K. T. Winther, M. J. Hoffmann, J. R. Boes, O. Mamun, M. Bajdich, and T. Bligaard, “Catalysis-Hub. org, an open electronic structure database for surface reactions”, Scientific Data, 6, 75 (2019); Catalysis Hub, https://catalysis-hub.org; J. O’Mara, B. Meredig, and K. Michel, “Materials data infrastructure: a case study of the citrination platform to examine data import, storage, and access”, JOM, 68, 2031– 2034 (2016); Citrine Informatics, https://citrine.io; T. Bazhirov, M. Mohammadi, K. Ding, and S. Barabash, “Large-scale high-throughput computer-aided discovery of advanced materials using cloud computing”, in APS March Meeting Abstracts (2017); Exabyte.io, https://exabyte.io; Granta Design, http://grantadesign.com; X. Rozanska, J. J. Stewart, P. Ungerer, B. Leblanc, C. Freeman, P. Saxe, and E. Wimmer, “High-throughput calculations of molecular properties in the MedeA environment: Accuracy of PM7 in predicting vibrational frequencies, ideal gas entropies, heat capacities, and Gibbs free energies of organic molecules”, Journal of Chemical & Engineering Data, 59, 3136–3143 (2014); Materials Design Inc. https://materialsdesign.com; MaterialsZone, https://materials.zone. 4A. Bruix, J. T. Margraf, M. Andersen, and K. Reuter, “First-principles-based multiscale modelling of heterogeneous catalysis”, Nature Catalysis, 2, 659–670 (2019). 5M. D. Wilkinson, M. Dumontier, I. J. Aalbersberg, G. Appleton, M. Axton, A. Baak, N. Blomberg, J.-W. Boiten, L. B. da Silva Santos, P. E. Bourne, J. Bouwman, A. J. Brookes, T. Clark, M. Crosas, I. Dillo, O. Dumon, S. Edmunds, C. T. Evelo, R. Finkers, A. Gonzalez-Beltran, A. J. G. Gray, P. Groth, C. Goble, J. S. Grethe, J. Heringa, P. A. C. ’t Hoen, R. Hooft, T. Kuhn, R. Kok, J. Kok, S. J. Lusher, M. E. Martone, A. Mons, A. L. Packer, B. Persson, P. Rocca-Serra, M. Roos, R. van Schaik, S.-A. Sansone, E. Schultes, T. Sengstag, T. Slater, G. Strawn, M. A. Swertz, M. Thompson, J. van der Lei, E. van Mulligen, J. Velterop, A. Waagmeester, P. Wittenburg, K. Wolstencroft, J. Zhao, and B. Mons, “The FAIR Guiding Principles for scientific data management and stewardship”, Scientific Data, 3, 160018 (2016). 6L. M. Ghiringhelli, C. Carbogno, S. Levchenko, F. Mohamed, G. Huhs, M. Lu ̈ders, M. Oliveira, and M. Scheffler, “Towards efficient data exchange and sharing for big-data driven materials science: metadata and data formats”, NPJ Computational Materials, 3, 46 (2017). 7C. Sutton, L. M. Ghiringhelli, T. Yamamoto, Y. Lysogorskiy, L. Blumenthal, T. Hammerschmidt, J. Golebiowski, X. Liu, A. Ziletti, and M. Scheffler, “NOMAD 2018 Kaggle Competition: Solving Materials Science Challenges Through Crowd Sourcing”, arXiv: 1812.00085 (2018). 8M. Ceriotti, M. J. Willatt, and G. Cs ́anyi, “Machine learning of atomic-scale properties based on physical principles”, Handbook of Materials Modeling: Methods: Theory and Modeling, 1–27 (2018). 9M. Todorovi ́c, M. U. Gutmann, J. Corander, and P. Rinke, “Bayesian inference of atomistic structure in functional materials”, NPJ Computational Materials, 5, 35 (2019). 90

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