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Machine learning and dimensional analysis assisted predictive models

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dc.contributor.advisor Waghmare, Umesh V.
dc.contributor.author Kumar, Narendra
dc.date.accessioned 2020-07-21T15:00:00Z
dc.date.available 2020-07-21T15:00:00Z
dc.date.issued 2018
dc.identifier.citation Kumar, Narendra. 2018, Machine learning and dimensional analysis assisted predictive models, MS thesis, Jawaharlal Nehru Centre for Advanced Scientific Research, Bengaluru en_US
dc.identifier.uri https://libjncir.jncasr.ac.in/xmlui/handle/10572/3026
dc.description Open access en_US
dc.description.abstract The 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.iso English en_US
dc.publisher Jawaharlal Nehru Centre for Advanced Scientific Research en_US
dc.rights © 2018 JNCASR en_US
dc.subject Dielectric material. en_US
dc.title Machine learning and dimensional analysis assisted predictive models en_US
dc.type Thesis en_US
dc.type.qualificationlevel Master en_US
dc.type.qualificationname MS en_US
dc.publisher.department Chemistry and Physics of Materials Unit (CPMU) en_US


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