Digital twins are revolutionizing the way that we approach innovation and optimize the performance of complex physical machines, devices, and components. Digital twins offer unparalleled insight into its form and function over time and across dimensions by providing a precise virtual replica of a physical object with real-time input.

With the complex multiscale systems and unique structural details, materials can be accurately represented in a digital twin. This virtual model captures not only the details of the material's composition at different scales but also its response to external factors. A holistic view of how materials behave in real-world scenarios can be provided by digital twins, considering both process history and in-service environment.

However, the development of the digital twin concept in systems design remains a challenge. Further research is needed to realize new values of digital twins in enhancing operational efficiency, achieving multi-field optimization, and enabling effective design of different materials and engineering systems.

Looking into the future, AI-enabled digital twins will further revolutionize the future of industrial and scientific solutions.