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the amount of data relevant to heterogeneous catalysis is still very small and the documentation of many data is very incomplete. Thus, there is an urgent need to create more data that is reliable and well-described. It is crucial that both computational and experimental researchers increase their efforts in establishing benchmarks and assessing the accuracy and precision of their studies. Eventually, AI tools can be applied for analyzing the data on a large scale to help the design new experiments and predict statistically exceptional materials that fulfill the conditions of a promising catalyst (e.g. stability, synthesizability, selectivity, activity). In this process, we also foresee an exciting opportunity for even closer collaboration (if not a complete merging) of computational and experimental studies. While important contributions from the discussed AI applications will come in the following years, significant breakthroughs can be expected during the next decade and beyond. References 1C. Draxl and M. Scheffler, “Big Data-Driven Materials Science and Its FAIR Data Infrastructure”, Hand- book of Materials Modeling: Methods: Theory and Modeling, arXiv: 1904.05859, 1–25 (2019). 2L. Himanen, A. Geurts, A. S. Foster, and P. Rinke, “Data-driven materials science: status, challenges and perspectives”, arXiv: 1907.05644 (2019). 3S. Curtarolo, W. Setyawan, S. Wang, J. Xue, K. Yang, R. H. Taylor, L. J. Nelson, G. L. W. Hart, S. Sanvito, M. Buongiorno-Nardelli, N. Mingo, and O. Levy, “AFLOWLIB.ORG: A distributed materials properties repository from high-throughput ab initio calculations”, Computational Materials Science, 58, 227–235 (2012); AFLOW, http://aflowlib.org; D. D. Landis, J. S. Hummelshoj, S. Nestorov, J. Greeley, M. Dulak, T. Bligaard, J. K. Norskov, and K. W. Jacobsen, “The computational materials repository”, Computing in Science & Engineering, 14, 51 (2012); Computational Materials Repository, https://cmr. fysik.dtu.dk; S. Graˇzulis, A. Daˇskeviˇc, A. Merkys, D. Chateigner, L. Lutterotti, M. Quiros, N. R. Serebryanaya, P. Moeck, R. T. Downs, and A. Le Bail, “Crystallography Open Database (COD): an open-access collection of crystal structures and platform for world-wide collaboration”, Nucleic Acids Research, 40, D420–D427 (2011); Crystallography Open Database, http://crystallography.net; A. Zakutayev, N. Wunder, M. Schwarting, J. D. Perkins, R. White, K. Munch, W. Tumas, and C. Phillips, “An open experimental database for exploring inorganic materials”, Scientific Data, 5, 180053 (2018); HTEM, https://htem.nrel.gov; C. Kim, A. Chandrasekaran, T. D. Huan, D. Das, and R. Ramprasad, “Polymer genome: a data-powered polymer informatics platform for property predictions”, The Journal of Physical Chemistry C, 122, 17575–17585 (2018); Khazana, https://khazana.gatech.edu; G. Pizzi, A. Cepellotti, R. Sabatini, N. Marzari, and B. Kozinsky, “AiiDA: automated interactive infrastructure and database for computational science”, Computational Materials Science, 111, 218–230 (2016); Materials Cloud, https://materialscloud.org; B. Blaiszik, K. Chard, J. Pruyne, R. Ananthakrishnan, S. Tuecke, and I. Foster, “The Materials Data Facility: Data services to advance materials science research”, JOM , 68, 2045–2052 (2016); The Materials Data Facility, https://materialsdatafacility.org.; A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K. A. Persson, “Commentary: The Materials Project: A materials genome approach to accelerating 89PDF Image | sustainable production of fuels and chemicals
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