Neural Network-Driven Atomistic Simulations for Predicting Chiroptical Properties in Ligan — NSF Award to University of California
Eran Rabani of the University of California, Berkeley, is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop advanced computational models that predict the optical properties of chiral semiconductor nanocrystals. These materials, which are es
| Award title | Neural Network-Driven Atomistic Simulations for Predicting Chiroptical Properties in Ligan |
|---|---|
| Award ID | 2449564 |
| Awardee | University of California-Berkeley |
| City | BERKELEY |
| State | CA |
| Amount obligated | $598,414 |
| Principal investigator | Eran Rabani |
| Program | Chem Thry, Mdls & Cmptnl Mthds |
| Start date | 05/01/2025 |
| Abstract | Eran Rabani of the University of California, Berkeley, is supported by an award from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry to develop advanced computational models that predict the optical properties of chiral semiconductor nanocrystals. These materials, which are essential components in next-generation technologies such as filters, sensors, and displays can be engineered to exhibit specific properties by controlling their size, shape, and sur |
| Source | NSF Awards |
$799/mo
Try NSFGrants →