Interpretable Augmented Intelligence for Multiscale Materials Discovery
Build an effective and interpretable learning framework for materials design across scales.
Conventional multiscale modeling is expensive and has limited applications due to difficulties across scales.
The convergence of data and materials science enables data-driven multiscale modeling that is faster, reliable, predictive, and interpretable. We are at the tipping point of inventing the future of materials research.
In collaborations between
This material is based upon work supported by the National Science Foundation under Grant No. OAC-1940125, 1940335, 1940114, 1940203, 1940107.