I-Corps: Translation Potential of Machine Learning to Optimize Polymer Nanocomposite Solutions for Thermal Management and Moisture Protection

This I-Corps project focusses on the commercialization of a software solution to optimize the selection of materials used to design and develop solar panels. Software solutions have the potential to significantly enhance the efficiency and longevity of solar panels by preventing overheating, which is a critical challenge in the solar energy sector. Rapid and accurate material selection can also be applied to other industries requiring thermal management solutions, including rechargeable batteries, semiconductors, and electronics. The increasing demand for improved energy efficiency, coupled with the scalability of the software, could offer a more cost-effective solution for improving the functionality, reliability and durability of solar panels, energy storage devices, and other electronics. This I-Corps project utilizes experiential learning coupled with a first-hand investigation of the industry ecosystem to assess the translation potential of a proprietary machine learning (ML) algorithm to identify, design, develop, and synthesize polymer nanocomposites. These materials address critical challenges in thermal management and barrier protection for a range of industries, including solar, rechargeable batteries, semiconductors, and supercapacitors. By utilizing machine learning modeling for material classification, the design, development, and selection of materials with the desired properties is accelerated. These nanocomposites may reduce infrared absorption, provide UV shielding, and perform as a barrier against oxygen, moisture, and other contamination, improving the functionality, reliability, and durability of solar panels. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.