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master's thesis
Istraživanje materijala primjenom strojnog učenja
Zagreb: University of Zagreb, Faculty of Science, 2019. urn:nbn:hr:217:559686
Sitar, Mia
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Sitar, M. (2019). Istraživanje materijala primjenom strojnog učenja (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:559686

Sitar, Mia. "Istraživanje materijala primjenom strojnog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:559686

Sitar, Mia. "Istraživanje materijala primjenom strojnog učenja." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:559686

Sitar, M. (2019). 'Istraživanje materijala primjenom strojnog učenja', Master's thesis, University of Zagreb, Faculty of Science, accessed 13 May 2024, https://urn.nsk.hr/urn:nbn:hr:217:559686

Sitar M. Istraživanje materijala primjenom strojnog učenja [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2019 [cited 2024 May 13] Available at: https://urn.nsk.hr/urn:nbn:hr:217:559686

M. Sitar, "Istraživanje materijala primjenom strojnog učenja", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:217:559686

Metadata
TitleIstraživanje materijala primjenom strojnog učenja
Title (english)Study of materials using machine learning
AuthorMia Sitar
MentorGoranka Bilalbegović (mentor)
Committee memberGoranka Bilalbegović (predsjednik povjerenstva)
Committee memberMario Basletić (član povjerenstva)
Committee memberIvan Kupčić (član povjerenstva)
Committee memberMihael Srđan Grbić (član povjerenstva)
Committee memberDanko Radić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2019-09-23, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
Iskorištavanje sunčeve energije predstavlja jedan od ključnih elemenata prelaska na čistu energiju. Perovskitni spojevi su medju najistraživanijim i najperspektivnijim materijalima za izradu solarnih ćelija. U ovom radu su algoritmima nadziranog strojnog učenja predvideni energijski procjep, standardna toplina stvaranja i najstabilnija faza kristalne rešetke perovskita. Korišteni su algoritmi L2 regularizirane linearne regresije i stabla odlučivanja. Pronađeni su perovskiti koji bi mogli biti pogodni kao gradivni materijal za solarne ćelije te su uspoređeni s onima koji se trenutno najčešće koriste u tu svrhu. U metodičkom dijelu rada opisano je nekoliko pokusa sa solarnim ćelijama koji bi se mogli izvesti u nastavi s ciljem naglašavanja važnosti solarne energije i informiranja učenika o temeljnim značajkama solarnih ćelija.
Abstract (english)
Exploitation of solar energy is one of the key elements for a clean energy transition. Perovskite compounds are among the most researched and promising materials for the solar cell production. In this diploma thesis supervised machine learning algorithms have been used to predict the band gap, heat of formation and the most stable phase of the crystal lattice for perovskites. Predictions were made with the L2 regularized linear regression and decision tree algorithms. Perovskites which might be suitable for the production of solar cells were found. They have been compared with ones currently used for this purpose. In the methodical section of this thesis a few solar cell experiments have been decribed, with the goal of emphasizing the importance of solar energy and introducing students to basic features of solar cells.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:559686
Study programmeTitle: Physics and Computer Science; specializations in: Education
Course: Education
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: magistar/magistra fizike i informatike (magistar/magistra fizike i informatike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2020-01-07 09:44:51