I-AIM: Interpretable Augmented Intelligence for Multiscale Material Discovery
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I-AIM

Interpretable Augmented Intelligence for ​Multiscale Materials Discovery
Build an effective and interpretable learning framework for materials design across scales.
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Conventional multiscale modeling is expensive and has limited applications due to difficulties across scales.
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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.

Our Approach


In collaborations between

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This material is based upon work supported by the National Science Foundation under Grant No. OAC-1940125,
1940335, 1940114, 1940203, 1940107.

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