Engineers Apply Physics-informed Machine Learning To Solar Cell Production - AI Summary

Despite the recent advances in the power conversion efficiency of organic solar cells, insights into the processing-driven thermo-mechanical stability of bulk heterojunction active layers are helping to advance the field. Lehigh University engineer Ganesh Balasubramanian, like many others, wondered if there were ways to improve the design of solar cells to make them more efficient? Balasubramanian, an associate professor of Mechanical Engineering and Mechanics, studies the basic physics of the materials at the heart of solar energy conversion – the organic polymers passing electrons from molecule to molecule so they can be stored and harnessed – as well as the manufacturing processes that produce commercial solar cells. Using the Frontera supercomputer at the Texas Advanced Computing Center (TACC) – one of the most powerful on the planet – Balasubramanian and his graduate student Joydeep Munshi have been running molecular models of organic solar cell production processes, and designing a framework to determine the optimal engineering choices. "When engineers make solar cells, they mix two organic molecules in a solvent and evaporate the solvent to create a mixture which helps with the exciton conversion and electron transport," Balasubramanian said.