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master's thesis
Molekularna dinamika na strojno naučenoj plohi potencijalne energije
Zagreb: University of Zagreb, Faculty of Science, 2019. urn:nbn:hr:217:000337
Ovčar, Juraj
University of Zagreb
Faculty of Science
Department of Physics

Institutional repository: Repository of the Faculty of Science

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Ovčar, J. (2019). Molekularna dinamika na strojno naučenoj plohi potencijalne energije (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:000337

Ovčar, Juraj. "Molekularna dinamika na strojno naučenoj plohi potencijalne energije." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:000337

Ovčar, Juraj. "Molekularna dinamika na strojno naučenoj plohi potencijalne energije." Master's thesis, University of Zagreb, Faculty of Science, 2019. https://urn.nsk.hr/urn:nbn:hr:217:000337

Ovčar, J. (2019). 'Molekularna dinamika na strojno naučenoj plohi potencijalne energije', Master's thesis, University of Zagreb, Faculty of Science, accessed 03 July 2024, https://urn.nsk.hr/urn:nbn:hr:217:000337

Ovčar J. Molekularna dinamika na strojno naučenoj plohi potencijalne energije [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2019 [cited 2024 July 03] Available at: https://urn.nsk.hr/urn:nbn:hr:217:000337

J. Ovčar, "Molekularna dinamika na strojno naučenoj plohi potencijalne energije", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2019. Available at: https://urn.nsk.hr/urn:nbn:hr:217:000337

Metadata
TitleMolekularna dinamika na strojno naučenoj plohi potencijalne energije
Title (english)Molecular dynamics on machine-learned potential energy surface
AuthorJuraj Ovčar
MentorIvor Lončarić (mentor)
MentorIvo Batistić (mentor)
Committee memberIvo Batistić (predsjednik povjerenstva)
Committee memberIvor Lončarić (član povjerenstva)
Committee memberŽeljko Skoko (član povjerenstva)
Committee memberDavor Horvatić (član povjerenstva)
Committee memberDamir Pajić (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Science
(Department of Physics)
Zagreb
Defense date and country2019-07-16, Croatia
Scientific / art field,
discipline and subdiscipline		NATURAL SCIENCES
Physics
Abstract
Za modeliranje materijala iz prvih principa danas se predominantno koristi teorija funkcionala gustoće, koja pruža dobar omjer preciznosti i računalne efikasnosti. Međutim, zbog trenutnih računalnih ograničenja, obično je prezahtjevno modelirati dinamiku sustava s desecima atoma. Jedna od obećavajućih metoda za približavanje simulacija realnim sustavima i povećanje skala na kojima je moguće provoditi dinamiku je interpolacija potencijala dobivenog s teorijom funkcionala gustoće pomoću neuronskih mreža. U ovom diplomskom radu smo simulirali sustav molekula ugljičnog monoksida adsorbiranih na površini paladija ozračenoj femtosekundnim laserskim pulsom. Prikazali smo proces strojnog učenja te implementirali molekularnodinamičke simulacije u modelu Langevinove dinamike s vremenski ovisnim temperaturama.
Abstract (english)
Nowadays, density functional theory is the predominant tool used in first-principles material modeling, providing an adequate ratio between precision and computational efficiency. However, because of current computational limitations, it is usually too demanding to simulate dynamics of systems consisting of tens of atoms. A promising method for reducing the gap between simulations and realistic systems and extending the scales across which it is possible to simulate dynamics is interpolation of potentials calculated with density functional theory via neural networks. In this master’s thesis we have simulated a system of carbon monoxide molecules adsorbed on palladium irradiated with a femtosecond laser pulse. We have shown the process of machine-learning and implemented molecular-dynamics simulations in the Langevin dynamics model with time dependent temperatures.
Keywords
Keywords (english)
Languagecroatian
URN:NBNurn:nbn:hr:217:000337
Study programmeTitle: Physics; specializations in: Research
Course: Research
Study programme type: university
Study level: integrated undergraduate and graduate
Academic / professional title: magistar/magistra fizike (magistar/magistra fizike)
Type of resourceText
File originBorn digital
Access conditionsOpen access
Terms of useLicence In copyright icon
Created on2019-09-04 11:42:10