Inverse Design of Materials
Description
Discovering new materials is typically a mix of art and science, with timelines to create and robustly test a new material mix / manufacturing method ranging from ten to twenty years. This project seeks to enable rapid discovery, optimization, qualifaction and deployment of fit-for-purpose materials.
Detailed example
Outputs include recipes and approaches for new materials custom-tailored to applications with an 4x speedup for the overall materials discovery / design lifecycle, and potential 10x throughput for the same cycle based on parallizing discovery of multiple materials at once.
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
The project is currently being utilized in an NESC investigation to improve SLS core stage weld quality. The technology will be used to select experiments for a fully autonomous robotic lab that is currently being procured to design better insulating materials for electrified aircraft. Bayesian optimization frameworks predicts the next best simulation to run to minimize a target. For the PMC example, for instance, the objective was to minimize weight while maintaining a minimum margin of safety.
Controls / human review
ATO: Not reported; PIA: Not published