Ganesh Balasubramanian uses TACC’s Frontera supercomputer to simulate photovoltaic fabrication, train AI to optimize energy production

This story originally appeared on the Texas Advanced Computing Center (The University of Texas at Austin) website.

Today, solar energy provides 2 percent of U.S. power. However, by 2050, renewables are predicted to be the most used energy source (surpassing petroleum and other liquids, natural gas, and coal) and solar will overtake wind as the leading source of renewable power. To reach that point, and to make solar power more affordable, solar technologies still require a number of breakthroughs. One is the ability to more efficiently transform photons of light from the Sun into usable energy.

Organic photovoltaics max out at 15 to 20 percent efficiency—substantial, but a limit on solar energy's potential. 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. They described the computational effort and associated findings in the May issue of IEEE Computing in Science and Engineering.

"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. "We mimicked how these cells are created, in particular the bulk heterojunction—the absorption layer of a solar cell. Basically, we're trying to understand how structure changes correlate with the efficiency of the solar conversion?"

Balasubramanian uses what he calls "physics-informed machine learning." His research combines coarse-grained simulation—using approximate molecular models that represent the organic materials—and machine learning. Balasubramanian believes the combination helps prevent artificial intelligence from coming up with unrealistic solutions.

"A lot of research uses machine learning on raw data," Balasubramanian said. "But more and more, there's an interest in using physics-educated machine learning. That's where I think lies the most benefit. Machine learning per se is simply mathematics. There's not a lot of real physics involved in it."

Writing in Computational Materials Science in February 2021, Balasubramanian and Munshi along with Wei Chen (Northwestern University), and TeYu Chien (University of Wyoming) described results from a set of virtual experiments on Frontera testing the effects of various design changes. These included altering the proportion of donor and receptor molecules in the bulk heterojunctions, and the temperature and amount of time spent in annealing—a cooling and hardening process that contributes to the stability of the product.

They harnessed the data to train a class of machine learning algorithms known as support vector machines to identify parameters in the materials and production process that would generate the most energy conversion efficiency, while maintaining structural strength and stability. Coupling these methods together, Balasubramanian's team was able to reduce the time required to reach an optimal process by 40 percent.

"At the end of the day, molecular dynamics is the physical engine. That's what captures the fundamental physics," he said. "Machine learning looks at numbers and patterns, and evolutionary algorithms facilitate the simulations."

Read the full story on the TACC website. 

Story by Aaron Dubrow (TACC Science and Technology Writer)

Ganesh Balasubramanian

Ganesh Balasubramanian, P.C. Rossin Associate Professor of Mechanical Engineering and Mechanics, Lehigh University

Solar cell illustration Credit: MechE Dept

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.