Research InterestsHigh throughput DFT, machine learning, thermoelectric materials, half-heuslers 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.
Biography B.S. Chemical Engineering, Carnegie Mellon University, 2018 Publications |