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efficient materials design. While these techniques are relatively established for homogeneous catal- ysis, their implementation remains a challenge for heterogeneous catalysis. Furthermore, these data can be collected in large databases with information about materials and further analyzed using artificial intelligence (AI) techniques. However, very substantial challenges remain. The working catalyst is a complex, dynamical system, driven by many intricate processes, and to obtain a full characterization of the catalytic behavior under operating conditions including bulk and surface structures, catalyst/support in- terfaces, defects, molecular modifications is essentially impossible. Even identifying all important descriptive parameters of the catalyst and the overall conversion process is a major challenge. 8.2 State of the art and scientific challenges Catalysis science is entering an era where the generation of new data from experiment and com- putation can no longer be handled by established methods. This is reflected in the so-called 4 V challenge, which identifies four characteristics of newly generated data that are rapidly growing: Volume (the amount of data), Variety (the heterogeneity of form and meaning of data), Velocity (the rate at which data may change or new data arrive), and Veracity (the uncertainty of data quality). Fortunately, big data of materials science offers not only challenges but also novel, ex- traordinary, and expansive opportunities for achieving new scientific knowledge and insight. These opportunities require new research concepts and lines of thought (for a recent review see reference [1] and references therein). Below, we provide a perspective on the state of the art and challenges associated with three areas that are central to the data-driven design of catalysts. 8.2.1 Data sharing Currently, the common approach to sharing and storing data in catalysis science is to publish results as focused research studies, reporting only those few data that are directly relevant for the particular topic. Even when many different materials and conditions are studied, very few results are shared with the community. Data that are not deemed immediately relevant are often kept private or even thrown away. Over the last few years, however, a change in scientific culture has begun, and the community has started to consider the extensive sharing of data. To some extent, major advancements have already been realized for computationally generated materials science data, and the experimental field has been discussing analogous concepts for about a year. To achieve the goal of sustainable fuels and chemicals, this process needs to be accelerated and more directly involve the catalysis science community. We will now briefly sketch the state of the art of data sharing for computational catalysis science (and materials science in general) and identify significant challenges in translating that progress to experimental catalysis science. 80PDF Image | sustainable production of fuels and chemicals
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