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master's thesis
Aktivno učenje interatomskih potencijala za predikciju kristalne strukture
Zagreb: University of Zagreb, Faculty of Science, 2023. urn:nbn:hr:217:041725
Jurdana, Janko
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

Cite this document

Jurdana, J. (2023). Aktivno učenje interatomskih potencijala za predikciju kristalne strukture (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:041725

Jurdana, Janko. "Aktivno učenje interatomskih potencijala za predikciju kristalne strukture." Master's thesis, University of Zagreb, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:217:041725

Jurdana, Janko. "Aktivno učenje interatomskih potencijala za predikciju kristalne strukture." Master's thesis, University of Zagreb, Faculty of Science, 2023. https://urn.nsk.hr/urn:nbn:hr:217:041725

Jurdana, J. (2023). 'Aktivno učenje interatomskih potencijala za predikciju kristalne strukture', Master's thesis, University of Zagreb, Faculty of Science, accessed 14 July 2024, https://urn.nsk.hr/urn:nbn:hr:217:041725

Jurdana J. Aktivno učenje interatomskih potencijala za predikciju kristalne strukture [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2023 [cited 2024 July 14] Available at: https://urn.nsk.hr/urn:nbn:hr:217:041725

J. Jurdana, "Aktivno učenje interatomskih potencijala za predikciju kristalne strukture", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2023. Available at: https://urn.nsk.hr/urn:nbn:hr:217:041725

Metadata
TitleAktivno učenje interatomskih potencijala za predikciju kristalne strukture
Title (english)Active learning of interatomic potentials for crystal structure prediction
AuthorJanko Jurdana
MentorIvor Lončarić (mentor)
MentorDanko Radić (komentor)
Committee memberDanko Radić (predsjednik povjerenstva)
Committee memberIvor Lončarić (član povjerenstva)
Committee memberDavor Horvatić (član povjerenstva)
Committee memberAndrej Novak (član povjerenstva)
Committee memberMatko Glunčić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2023-07-13, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
Predikcija kristalne strukture predstavlja vrlo zahtjevan zadatak u modernoj fizici čvrstog stanja. Većina dosadašnjih metoda zasnovana je na teoriji funkcionala gustoće. Međutim, takve metode postaju računalno prezahtjevne kada su promatrani sustavi kristali organskih molekula. Razvojem metoda strojnog učenja, moguće je ubrzati izračune svojstava takvih sustava što otvara vrata proučavanju široke klase materijala. U ovom radu su takve tehnike iskorištene za predikciju energija osnovnog stanja i sila na atome treniranjem strojno naučenih potencijala. Svojstva izračunata tim tehnikama su potom uspoređena sa svojstvima dobivenim korištenjem metoda teorije funkcionala gustoće.
Abstract (english)
Crystal structure prediction poses a very demanding task in modern day solid state physics. Most of the existing methods are based on density functional theory. However, these methods become computationally too demanding when the studied systems are crystals of organic molecules. With the development of machine learning techniques, the calculations of the properties of such systems were drastically accelerated and thus opened the door to the study of a wide class of materials. In this paper, such methods were used for predicting ground state energies and forces on the atoms by training machine learned force fields. The properties calculated by these techniques were then compared with the properties obtained using density functional theory.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:041725
ProjectNumber: UIP-2020-02-5675
Title: Povećanje prostorne i vremenske skale modeliranja materijala iz prvih principa pomoću strojnog učenja
Title: Extending length and time scales of first principles material modelling via machine learning
Acronym: ExtMatModelML
Leader: Ivor Lončarić
Jurisdiction: Croatia
Funder: HRZZ
Funding stream: UIP
Study programmeTitle: The University Integrated Undergraduate and Graduate Programme in Physics; Research Physics; specializations in: Research Physics
Course: Research Physics
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: sveučilišni magistar fizike (sveučilišni magistar fizike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2024-02-15 14:32:22