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dc.contributor.advisorLi, Wei, doctor of mechanical engineering
dc.creatorYou, Zhuoya
dc.date.accessioned2019-03-04T21:08:47Z
dc.date.available2019-03-04T21:08:47Z
dc.date.created2018-12
dc.date.submittedDecember 2018
dc.identifier.urihttps://hdl.handle.net/2152/73535
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/685
dc.description.abstractWith the development of algorithms, models and data-driven efforts in other areas, machine learning is beginning to make impacts in materials science and engineering. In this work, we review the basic steps of using machine learning in materials science. We also develop several machine learning methods to predict the two physically-distinct properties of transparent conductors: formation enthalpy, which is an indication of stability, and bandgap energy, which is an indication of optical transparency. These include regression-based models such as the ordinary least squares (OLS) regression model, stepwise selection model, Ridge model and Lasso model, and tree-based models such as the random forest model and gradient boosted model (GBM). We discuss the advantages and potential problems of each model and provide suggestions for possible applications.
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.subjectMachine learning
dc.subjectStatistical analysis
dc.subjectRegression model
dc.subjectGBM model
dc.subjectMaterial property prediction
dc.titleMachine learning and statistical analysis in material property prediction
dc.typeThesis
dc.date.updated2019-03-04T21:08:48Z
dc.description.departmentMechanical Engineering
thesis.degree.departmentMechanical Engineering
thesis.degree.disciplineMechanical Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelMasters
thesis.degree.nameMaster of Science in Engineering
dc.creator.orcid0000-0003-4357-3653
dc.type.materialtext


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