Collaborative Research: MFS-SPEED: AI-Driven Design of Architecturally Varied and Deconstr — NSF Award to University of California
In this project, artificial intelligence (AI) will be used to design new sustainable polymeric materials with a range of properties and that can be recycled without the need for costly and inefficient separation from mixed waste streams. Today’s plastic waste challenge exists at a scale of megatons per day across tens
| Award title | Collaborative Research: MFS-SPEED: AI-Driven Design of Architecturally Varied and Deconstr |
|---|---|
| Award ID | 2519577 |
| Awardee | University of California-Berkeley |
| City | BERKELEY |
| State | CA |
| Amount obligated | $588,903 |
| Principal investigator | Brooks Abel |
| Program | OFFICE OF MULTIDISCIPLINARY AC, TIP-CHIPS KTA-10 Materials, CHEMISTRY PROJECTS |
| Start date | 09/01/2025 |
| Abstract | In this project, artificial intelligence (AI) will be used to design new sustainable polymeric materials with a range of properties and that can be recycled without the need for costly and inefficient separation from mixed waste streams. Today’s plastic waste challenge exists at a scale of megatons per day across tens of thousands of applications and products. The researchers will create new types of depolymerizable plastics derived from simple feedstocks, and they will develop physics-informed |
| Source | NSF Awards |
$799/mo
Try NSFGrants →