Capability Summary
Oak Ridge National Laboratory researchers are using the lab’s unmatched computing power to integrate multi-scale modeling, AI/ML, and exascale computing for predicting and validating materials behavior across vast swaths of time and size scales, enabling rapid mechanistic understanding, accelerated discovery, and validation of laboratory results.
To fully grasp the characteristics of various materials, researchers must understand material behavior at a full range of time and size scales. ORNL researchers, through multi-scale modeling and intelligent materials approaches, are using supercomputing, including ORNL’s Frontier, to model materials across scales - from the atomic level, where the fundamental quantum properties of material understanding is necessary, to the macro scale where materials performance is realized. Similarly, the effort’s multi-scale modeling allows researchers to investigate materials at time scales ranging from femtoseconds to years.
Covering this vast range of time and size scales experimentally in a laboratory could take lifetimes, but the computing power available at ORNL allows the research to be done quickly in computer modeling. This approach allows for mechanistic discovery and predictive design, enabling scientists to rapidly identify and refine materials with desirable characteristics. These results can then be verified experimentally in the lab. The inverse is true, with many researchers across ORNL turning to multi-scale modeling and computational and data analytics workflows to help understand lab results that don’t have easy explanations.
The program is also moving into artificial intelligence and machine learning (AI/ML) as a central enabler. Custom ORNL-developed tools – such as ORNLCompMat (workflow to explore quantum materials), ASCENDS (AI/ML workflow), Equilipy (scalable thermodynamics), and Myna (digital twin for additive manufacturing) – leverage ORNL’s decades of high-quality experimental and theoretical data to train robust AI models. This ensures predictive accuracy, scalability, and accelerated discovery across multiple application domains.
Areas of Material Exploration
- Quantum materials for computing, sensing, and multi-functional applications
- Lightweight and high-strength materials for next-generation transportation and structural applications
- Improved energy storage systems, including batteries and beyond-lithium technologies
- Next-generation alloys for electrical transmission, extreme environments, and high-performance devices
- Additive manufacturing with digital-twin modeling for predictive build quality and defect mitigation
- Radiation-tolerant materials for nuclear and fusion
