EXTENDED ABSTRACT: Materials Genome Engineering (MGE) represents a pivotal approach for rapidly acquiring experimental and simulation data on materials, while Artiffcial Intelligence (AI) serves as a powerful tool for deeply mining and fully exploiting materials data value. The combination of AI and MGE is reshaping the paradigm of materials R&D (research and development). It not only improves the efficiency and quality of R&D but also contributes to enhancing material properties, as well as boosting the predictability and specificity of materials design. The new materials R&D paradigm based on the "AI+MGE" technological route along with its industrial application cases is introduced in this report. This paradigm integrates modules such as intelligent materials database, multi-scale simulation tools, high-throughput computing workffow engines and AI algorithms. The intelligent materials database, whose sourcing data from independently controllable origins, can be combined with AI for materials screening and performance prediction. Multi-scale simulation tools, in conjunction with high-throughput computing workflow engines, enable efficient materials design and screening, helping enterprises reduce costs and enhance efffciency. Successful application cases of this research paradigm in ffelds such as Li+ batteries, chemicals, alloys and steels, and solar cells will be displayed in this report. These cases include electrolyte screening, polymer performance prediction, and alloy materials design.

Keywords: MGE; AI; Materials database; Multi-scale simulation; high-throughput computing