Lattice dynamics and thermal transport phenomena are ubiquitous and play a critical role in the performance of various microelectronic devices and energy-conversion materials, such as semiconductors, thermoelectrics, and thermal barrier coating materials. Developing an in-depth understanding of the underlying atomistic mechanisms, advanced strategies for accelerated materials discovery, and machine learning models with improved accuracy and transferability is therefore of urgent need, yet remains limited.
My research addresses these challenges by developing advanced first-principles theories for phonon dynamics and heat transfer using density functional theory, constructing quantum materials phonon database via high-throughput computing, and building unified graph neural networks for interatomic interactions. In this talk, I will first show how lattice dynamics calculations considering higher-order phonon-phonon interactions pave the way for an unprecedented understanding of the microscopic heat transfer mechanisms in crystalline semiconductors. Then I will discuss how to effectively discover materials with targeted ultralow thermal conductivity for thermoelectric applications through integrating high-throughput computing with learned chemical physics. Finally, I will demonstrate a theory-guided, data-driven understanding of the lower limit of lattice thermal conductivity in all inorganic solids. Leveraging machine learning models rooted in graph neural network architectures, we can now accurately forecast heat transfer phenomena in amorphous materials. I will also briefly comment on other potential applications of our approaches and future research directions.
Dr. Yi Xia is a faculty member in the Department of Mechanical & Materials Engineering at the Maseeh College of Engineering and Computer Science, Portland State University, since 2022. He specializes in first-principles simulations of dynamical and transport properties of phonons and electrons, leveraging high-throughput computing and machine learning to address complex materials science problems. He is the recipient of the NSF LEAPS-MPS Award 2023. Dr. Xia earned his Ph.D. in Materials Science and Engineering in 2016 from the University of California, Los Angeles, under the mentorship of Prof. Vidvuds Ozolins, and his B.S. in condensed matter physics in 2011 from the University of Science and Technology of China. Following his doctoral studies, he pursued postdoctoral research with Prof. Chris Wolverton at Northwestern University and Dr. Maria Chan at Argonne National Laboratory.