This $360,835 National Science Foundation project grant supports collaborative research between University of Missouri-Columbia and Carnegie Mellon University to develop a novel data-driven framework for the de novo generation of molecules with targeted physical and chemical properties. Led by Professors Jian Lin, Shih-Kang Chao of University of Missouri-Columbia and Olexandr Isayev of Carnegie Mellon University, the three-year award under the NSF's Mathematical and Physical Sciences program (CFDA 47.049) will combine generative modeling, reinforcement learning, and active learning algorithms to create a general methodology for property-objective inverse molecular design. This integrated data-driven approach is intended to design novel molecules that meet multiple optimized physicochemical, electronic, optical, and redox properties for applications in areas such as medicine, photovoltaics, catalysis, thermal storage, and organic redox flow batteries.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $0 | 8/2/22 | ||
| Not listed | $360.8k | 3/30/22 |