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dc.contributor.advisorGerbrand Ceder.en_US
dc.contributor.authorRong, Ziqinen_US
dc.contributor.otherMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.en_US
dc.date.accessioned2017-09-15T14:19:58Z
dc.date.available2017-09-15T14:19:58Z
dc.date.copyright2016en_US
dc.date.issued2016en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/111221
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Materials Science and Engineering, 2016.en_US
dc.descriptionThesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2016.en_US
dc.descriptionThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.en_US
dc.descriptionCataloged from student-submitted PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 45-52).en_US
dc.description.abstractAccelerating the discovery of advanced materials is essential for human beings. However, the traditional trial-and-error way of developing materials is often very empirical and time- consuming. In 2011, the launch of Materials Genome Initiative marked a large-scale collaboration between computer scientists and materials scientists to deploy proven computational methods to predict, screen, and optimize materials at an unparalleled scale and rate. This thesis is based on this idea. Finding a suitable cathode material for Mg batteries has been one of the key challenges to the next-generation multi-valent battery technology. In this thesis, a high-throughput computation system is proposed to solve such problem. I tested the high-throughput structures applying traditional NEB calculations schemes and find out it is very different to scale traditional NEB method to a high-throughput application. Then I proposed a new scheme for estimating migration minimum- energy path (MEP) geometry and energetics (PathFinder and ApproxNEB). By testing our methodology against standard NEB calculations and literature values, we find that the PathFinder algorithm can reliably predict the geometry of cation migration MEP within 0.2 Å at negligible computational cost. Furthermore, we find that the ApproxNEB calculation scheme yields activation barriers for migration within an error bound of 20 meV while using significantly fewer computational resources than NEB. We envision that our methods can be used to accelerate NEB calculations, as well as to provide a robust estimation criterion for migration barriers in ionic materials for highthroughput computational screening of materials. Based upon these two newly developed methods, coupled with EndPointFinder, I developed two functional high-throughput applications (ApproxNEB for estimating migration barriers and PathFinder for calculating migration geometric paths), and have already put PathFinder high-throughput system into production and calculate around 2000 structures.en_US
dc.description.statementofresponsibilityby Ziqin Rong.en_US
dc.format.extent52 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectMaterials Science and Engineering.en_US
dc.subjectElectrical Engineering and Computer Science.en_US
dc.titleBetter multivalent battery materials through diffusion high-throughput computationsen_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
dc.contributor.departmentMassachusetts Institute of Technology. Department of Materials Science and Engineering
dc.identifier.oclc1002849345en_US


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