MEMS NEWS SPRING 2021
MECHANICAL ENGINEERING & MATERIALS SCIENCE
Annual Publication of the University of Pittsburgh Swanson School of Engineering
MEMS Congratulates Back-to-Back-to-Back Career Winners Sangyeop Lee: Using Machine Learning to Improve Energy Performance of Semiconductors and Insulators
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eveloping materials with ultrahigh or ultralow thermal conductivity along a certain direction can enable new energy storage and conversion devices. However, grain boundaries – two-dimensional defects in crystal structures – exist in polycrystalline material and significantly affect thermal transport. Addressing the defects is currently not efficient - observing and experimenting with grain boundaries when creating materials can prove to be a lengthy and costly process. However, machine learning may provide a more sustainable alternative.
“In real materials, atoms are disordered and it has been extremely challenging to predict how atoms vibrate in disordered structures from firstprinciples. However, machine learning can help us gain a quantitative understanding of thermal transport that can help us predict how a material will behave.”
Sangyeop Lee, assistant professor of mechanical engineering and materials science, received a $500,000 CAREER Award from the National Science Foundation for research that would utilize machine learning to model thermal transport in polycrystalline materials. The research seeks to create a computer model that can predict the conductive properties of a material in real life, providing guidance to engineer defects for desired thermal properties. “Thermal transport across grain boundaries is not well understood. Studying heat as it transfers across a material at the atomistic scale means observing how atoms vibrate,” explains Lee.
engineering.pitt.edu/mems
The improved understanding of thermal transfer across grain boundaries will enable engineers to create materials that convert heat to electricity more efficiently, for example, or better manage heat in electronic devices.