Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/429703
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dc.date.accessioned2022-12-21T13:23:35Z-
dc.date.available2022-12-21T13:23:35Z-
dc.identifier.urihttp://hdl.handle.net/10603/429703-
dc.description.abstractSearch for clean and renewable energy resources has driven recent interest in designing thermoelectric materials that convert the waste heat to useful electricity. High performance thermoelectric materials require excellent electronic transport and favorable thermal transport, simultaneously. Given the interdependence of various transport parameters, it is daunting to achieve desirable performance. We attempt to address some of these challenges using density functional theory in combination with machine-learning based approaches. We first report the decoupling of Seebeck coefficient and electrical conductivity by tuning the distortion parameter of chalcopyrites leading to complete convergence of bands, thereby resulting in unprecedented enhancement of electronic transport properties. A combination of excellent electronic transport and low thermal conductivity in CdGeAs2 results into a high ZT of 1.67 at 1000K. To find a system with low thermal conductivity, we study the oxychalcogenide system AgBiTeO, demonstrating the unique collective rattling motion hosted by chemical bond hierarchy. The favorable electronic and thermal transport properties result in a maximum ZT of 1.99 at 1200K, which is highest among the existing bulk oxide-based thermoelectric materials. Owing to the complexity and resource extensive calculations involved in determining electron relaxation time (and#964;el), we employed machine learning approach to estimate the and#964;el. The machine learning model uses data available for experimental electrical conductivity and a collection of accessible elemental information. This model with a rmse of 0.22, outperforms the deformation potential model, and performs adequately on the unseen data to predict the relaxation time over a wide range of temperatures. Further, we develop an effective descriptor by using chalcopyrite class of compounds, to guide an accelerated screening of materials with desirable degree of anharmonicity. The high-throughput study corroborates the role of a very simple parameter, phonon...
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dc.languageEnglish
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dc.rightsuniversity
dc.titleOvercoming Challenges Associated with Designing of Thermoelectric Materials DFT and Machine Learning Approaches
dc.title.alternative
dc.creator.researcherMukherjee, Madhubanti
dc.subject.keywordEngineering and Technology
dc.subject.keywordMaterial Science
dc.subject.keywordMaterials Science Multidisciplinary
dc.description.note
dc.contributor.guideSingh, Abhishek K
dc.publisher.placeBangalore
dc.publisher.universityIndian Institute of Science Bangalore
dc.publisher.institutionMaterials Research Centre
dc.date.registered
dc.date.completed2021
dc.date.awarded2021
dc.format.dimensions
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Materials Research Centre

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01_title.pdfAttached File63.9 kBAdobe PDFView/Open
02_prelim pages.pdf181.06 kBAdobe PDFView/Open
03_table of contents.pdf54.03 kBAdobe PDFView/Open
04_abstract.pdf54.95 kBAdobe PDFView/Open
05_chapter 1.pdf318.6 kBAdobe PDFView/Open
06_chapter 2.pdf347.04 kBAdobe PDFView/Open
07_chapter 3.pdf2.88 MBAdobe PDFView/Open
08_chapter 4.pdf2.29 MBAdobe PDFView/Open
09_chapter 5.pdf2.81 MBAdobe PDFView/Open
10__chapter 6.pdf7.28 MBAdobe PDFView/Open
11_chapter 7.pdf562.6 kBAdobe PDFView/Open
12_chapter 8.pdf72.49 kBAdobe PDFView/Open
13_annexure.pdf86.05 kBAdobe PDFView/Open
80_recommendation.pdf135.88 kBAdobe PDFView/Open


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