Material technology is the driving force of industrial development. The fourth generation material R&D model combines material database, theoretical calculation, and statistical methods to accelerate the development, discovery, and usage of new material. With regard to digital data, besides providing well-known databases, high throughput computing technology can be used to obtain big data on material properties. Data classification, data mining, and statistical analysis techniques are employed to clarify the correlation among processing-structure property (PSP). Furthermore, machine learning or deep learning is used to find physical correlations from data, which enables data driven material research.
- Scientific Breakthrough
- High throughput computing and dependent flow implementation for first principle calculation and molecular dynamics.
- Image reconstruction and deep learning pattern recognition and analysis technologies for materials images.
- Artificial intelligence aided Materials Innovation and Material properties predictor.
- Industry Applications
The application of AI and MGI technologies provides a new research paradigms to accelerate the deployment of advanced material in industry.
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