Material discovery is an ongoing process and has been a milestone in human progress.

Based on the physical space of the network, in the coordination and analysis of the knowledge of material theory, processing and performance present so far, simulation software using quantum mechanics and other material properties, Digital material data, intelligent robot learning algorithm, etc. to realize material prediction, design and discovery.

Tens of thousands of materials have different functional characteristics, real-time performance and ecological characteristics, and are often used in combination. There is a need to pool, organize, analyze, interpret, and search for a wide variety of real-time data on the various stages of material and material manufacturing. The material large data helps to bridge the gap between the multi-scale model and the multi-scale test. Use cheap sensors and faster, cheaper assembly power, cloud computing, open source and user-friend applications, large data analytics algorithms, machine learning and artificial intelligence, for future manufacturing opportunities for material data drive. The deep-seated advantages of various manufacturing processes and materials will help to improve productivity, process efficiency, and environmental performance of the product.

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