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. 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.
