First-Principles Calculations of Electronic, Optical, and Transport Properties of Materials for Energy Applications

Solar electricity is a reliable and environmentally friendly method of sustainable energy production and a realistic alternative to conventional fossil fuels. Moreover, thermoelectric energy conversion is a promising technology for solid-state refrigeration and efficient waste-heat recovery. Predicting and optimizing new photovoltaic and thermoelectric materials composed of Earth-abundant elements that exceed the current state of the art, and understanding how nanoscale structuring and ordering improves their energy conversion efficiency pose a challenge for materials scientists. I approach this challenge by developing and applying predictive high-performance computing methods to guide research and development of new materials for energy-conversion applications. %Advances in computer-simulation algorithms and high-performance computing resources promise to speed up the development of new compounds with desirable properties and significantly shorten the time delay between the discovery of new materials and their commercial deployment. I present my calculated results on the extraordinary properties of nanostructured semiconductor materials, including strong visible-light absorbance in nanoporous silicon and few-layer SnSe and GeSe. These findings highlight the capability of nanoscale structuring and ordering to improve the performance of Earth-abundant materials compared to their bulk counterparts for solar-cell applications. I also successfully identified the dominant mechanisms contributing to free-carrier absorption in n-type silicon. My findings help evaluate the impact of the energy loss from this absorption mechanism in doped silicon and are thus important for the design of silicon solar cells. In addition, I calculated the thermoelectric transport properties of p-type SnSe, a bulk material with a record thermoelectric figure of merit. I predicted the optimal temperatures and free-carrier concentrations for thermoelectric energy conversion, as well the theoretical upper limit of the figure of merit. I also determined the electronic structures and thermoelectric properties of Mg2Si, Mg2Ge, and Mg2Sn, a family of Earth-abundant thermoelectric compounds.% I uncovered the importance of quasiparticle corrections and the proper treatment of pseudopotentials in the determination of the band gaps and the thermoelectric transport properties at high temperatures. The methods and codes I developed in my research form a general predictive toolbox for the design and optimization of the functional properties of materials for energy applications.PHDMaterials Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/137058/1/guangsha_1.pd

A Data Science Approach to Understanding Residential Water Contamination in Flint

When the residents of Flint learned that lead had contaminated their water system, the local government made water-testing kits available to them free of charge. The city government published the results of these tests, creating a valuable dataset that is key to understanding the causes and extent of the lead contamination event in Flint. This is the nation's largest dataset on lead in a municipal water system. In this paper, we predict the lead contamination for each household's water supply, and we study several related aspects of Flint's water troubles, many of which generalize well beyond this one city. For example, we show that elevated lead risks can be (weakly) predicted from observable home attributes. Then we explore the factors associated with elevated lead. These risk assessments were developed in part via a crowd sourced prediction challenge at the University of Michigan. To inform Flint residents of these assessments, they have been incorporated into a web and mobile application funded by \texttt{Google.org}. We also explore questions of self-selection in the residential testing program, examining which factors are linked to when and how frequently residents voluntarily sample their water.Comment: Applied Data Science track paper at KDD 2017. For associated promotional video, see https://www.youtube.com/watch?v=0g66ImaV8A