ACED: Tail-aware Generative Modeling for Inverse Discovery of Molecules — NSF Award to Regents of the University of Michigan - Ann
Discovering new molecules that have desired properties will address critical technological challenges ranging from energy storage to drug development. The traditional trial-and-error approach of creating and testing molecules is expensive, inefficient, and time-consuming. Likewise, it is too computationally expensive t
| Award title | ACED: Tail-aware Generative Modeling for Inverse Discovery of Molecules |
|---|---|
| Award ID | 2435696 |
| Awardee | Regents of the University of Michigan - Ann Arbor |
| City | ANN ARBOR |
| State | MI |
| Amount obligated | $500,000 |
| Principal investigator | Yixin Wang |
| Program | ACED-Accl Comp Enabled Sci Dis |
| Start date | 07/15/2025 |
| Abstract | Discovering new molecules that have desired properties will address critical technological challenges ranging from energy storage to drug development. The traditional trial-and-error approach of creating and testing molecules is expensive, inefficient, and time-consuming. Likewise, it is too computationally expensive to use only quantum mechanical calculations to adequately screen the vast space of possible molecules for desired properties. In contrast, generative modeling based upon machine lea |
| Source | NSF Awards |
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