About this Research Topic
Data-driven materials design is rapidly becoming a major thrust in science and technology. The growing ability to produce large amounts of reliable and consistent theoretical and experimental data created new opportunities in using the data to gain understanding and uncover hidden correlations between material's properties of different levels of complexity. This is particularly helpful in the area of catalysis, where the relationship between target properties such as turnover frequency of a catalytic reaction and the material emerges as a result of interplay of processes with spatial and temporal scales spanning more than ten orders of magnitude. Data analytics with the help of artificial intelligence (AI) can play a pivotal role in bridging the multiple scales and establishing the complex relation between basic, easily obtainable features of a catalytic material on one hand, and its stability, catalytic activity, and selectivity on the other. To achieve this, three main ingredients are necessary: (i) reliable and consistent theoretical and experimental data, (ii) development of domain-specific data-analytics methods, and (iii) high-throughput screening of materials using AI models. These ingredients and their combinations will be the focus of the article collection.
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