Please use this identifier to cite or link to this item: https://libjncir.jncasr.ac.in/xmlui/handle/10572/3026
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dc.contributor.advisorWaghmare, Umesh V.-
dc.contributor.authorKumar, Narendra-
dc.date.accessioned2020-07-21T15:00:00Z-
dc.date.available2020-07-21T15:00:00Z-
dc.date.issued2018-
dc.identifier.citationKumar, Narendra. 2018, Machine learning and dimensional analysis assisted predictive models, MS thesis, Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluruen_US
dc.identifier.urihttps://libjncir.jncasr.ac.in/xmlui/handle/10572/3026-
dc.descriptionOpen accessen_US
dc.description.abstractThe success of machine learning approaches to high throughput screening for drug discovery in the pharmaceutical industry has inspired similar schemes for materials discovery [8, 13]. One of the challenges in adopting a datadriven approach to materials discovery is the lack of large experimental data, especially in the space of inorganic materials. To overcome this problem, hybrid approaches have been developed in which properties calculated from crystal structure by ab initio density functional theory are combined with experimental data [18]. These hybrid approaches have the best of both worlds in that erroneous DFT property calculations and limited experimental data compensate for each other.en_US
dc.language.isoEnglishen_US
dc.publisherJawaharlal Nehru Centre for Advanced Scientific Researchen_US
dc.rights© 2018 JNCASRen_US
dc.subjectDielectric material.en_US
dc.titleMachine learning and dimensional analysis assisted predictive modelsen_US
dc.typeThesisen_US
dc.type.qualificationlevelMasteren_US
dc.type.qualificationnameMSen_US
dc.publisher.departmentChemistry and Physics of Materials Unit (CPMU)en_US
Appears in Collections:Student Theses (CPMU)

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