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The chemical industry is increasingly focusing on Materials Informatics (MI), and the number of companies and researchers incorporating MI into their development processes is gradually rising. MI refers to the attempt to utilize digital technologies in product design within process-based manufacturing industries like the chemical sector. Advances in digital technologies such as big data, AI, and machine learning make it possible to analyze vast amounts of experiments and papers to predict material formulations, thereby improving the efficiency of material development. This report discusses the latest trends in the further utilization of AI beyond the realms of Materials Informatics (MI) and product formulation. It also includes case studies from major companies that have already begun initiatives in MI, such as Asahi Kasei, Sumitomo Chemical, Toray, and Yokohama Rubber. *The 55% reduction in man-hours associated with experiments is based on estimates.

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The wave of digital transformation (DX) is finally beginning to impact the upstream research and development in the manufacturing industry value chain. How will AI change the research and development in manufacturing, where it is said that 70% of designs rely on the intuition and experience of veteran researchers? This report explains the background of data-driven material development, which is also being utilized in universities and aerospace research institutions, and provides hints on how to reduce process time in design and material selection by 55%. *For more details, please download the catalog for confirmation. *The 55% reduction in material development process time is based on estimates.

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Are there any challenges like these in the field of design and development? - A vast amount of trial and error in product design - Lack of product design and technology transfer relying on skilled workers - Having data but struggling with how to utilize it By introducing "WALL," the following becomes possible! - Instant calculation of optimal design through machine learning - Optimization of parameters through machine learning The following benefits can be expected from implementation! - Significant reduction in design man-hours and prototyping costs - Elimination of dependency on individuals through data-driven product design

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