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Andrew Lee

Research Interests

High throughput DFT, machine learning, thermoelectric materials, half-heuslers

Current Research

Andrew uses high-throughput DFT and machine learning to model the stability and thermoelectric properties of half-heusler solid solution mixtures. His work is part of a collaboration group where computationalists predict candidate compositions for experimentalists to synthesize and characterize, which in turn help improve the computational models in a feedback loop fashion. 


B.S. Chemical Engineering, Carnegie Mellon University, 2018