The National Science Foundation (NSF) Division of Chemistry awarded a $449,440 Project Grant to Colorado State University for the purpose of "DESIGNING CHEMICAL PROCESSES WITH MULTICOMPONENT SOLVENTS THROUGH SELF-EVOLVING SOLUBILITY DATABASES AND NEURAL NETWORKS". This award is funded through the NSF's Mathematical and Physical Sciences program (CFDA 47.049). Under this grant, the research team led by Dr. Seonah Kim will develop innovative machine learning (ML) models for predicting solubility in complex multicomponent chemical systems. The key objectives are to build an extensive self-evolving solubility database integrating experimental and computational data, and to create graph neural network (GNN) models capable of rapidly and accurately predicting solubility for a wide range of solutes, including polymers, in single and multicomponent solvents. This research aims to advance the design of chemical processes involving solvents, which is critical for developing pharmaceuticals, renewable fuels, electrolytes, and other important chemical products and materials.
Mod # | Description | Reason For Modification | Federal Obligation (Click to sort descending) | Date (Click to sort ascending) |
|---|---|---|---|---|
| Not listed | $449.4k | 8/14/23 |