Prijenosno učenje interatomskih potencijala: od molekula do kristala

Parunov, Antonio

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master's thesis

Prijenosno učenje interatomskih potencijala: od molekula do kristala

Zagreb: University of Zagreb, Faculty of Science, 2022. urn:nbn:hr:217:686126

Parunov, Antonio

University of Zagreb
Faculty of Science
Department of Physics

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APA 6th Edition

Parunov, A. (2022). Prijenosno učenje interatomskih potencijala: od molekula do kristala (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:686126

MLA 8th Edition

Parunov, Antonio. "Prijenosno učenje interatomskih potencijala: od molekula do kristala." Master's thesis, University of Zagreb, Faculty of Science, 2022. https://urn.nsk.hr/urn:nbn:hr:217:686126

Chicago 17th Edition

Parunov, Antonio. "Prijenosno učenje interatomskih potencijala: od molekula do kristala." Master's thesis, University of Zagreb, Faculty of Science, 2022. https://urn.nsk.hr/urn:nbn:hr:217:686126

Harvard

Parunov, A. (2022). 'Prijenosno učenje interatomskih potencijala: od molekula do kristala', Master's thesis, University of Zagreb, Faculty of Science, accessed 25 December 2024, https://urn.nsk.hr/urn:nbn:hr:217:686126

Vancouver

Parunov A. Prijenosno učenje interatomskih potencijala: od molekula do kristala [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2022 [cited 2024 December 25] Available at: https://urn.nsk.hr/urn:nbn:hr:217:686126

IEEE

A. Parunov, "Prijenosno učenje interatomskih potencijala: od molekula do kristala", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2022. Available at: https://urn.nsk.hr/urn:nbn:hr:217:686126

Cite this item: https://urn.nsk.hr/urn:nbn:hr:217:686126

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Metadata

Title	Prijenosno učenje interatomskih potencijala: od molekula do kristala
Title (english)	Transfer learning of interatomic potentials: from molecules to crystals
Author	Antonio Parunov
Mentor	Ivor Lončarić (mentor)
Mentor	Danko Radić (komentor)
Committee member	Danko Radić (predsjednik povjerenstva)
Committee member	Ivor Lončarić (član povjerenstva)
Committee member	Mihael Srđan Grbić (član povjerenstva)
Committee member	Goranka Bilalbegović (član povjerenstva)
Committee member	Denis Sunko (član povjerenstva)
Granter	University of Zagreb Faculty of Science (Department of Physics) Zagreb
Defense date and country	2022-07-15, Croatia
Scientific / art field, discipline and subdiscipline	NATURAL SCIENCES Physics
Abstract	Strojno učenje ima mogućnost promijeniti paradigmu izračuna svojstava kompleksnih molekula i kristala. Dosadašnje precizne metode bazirane na teoriji funkcionala gustoće su računalno preskupe za složene zadatke kao što je predviđanje kristalnih struktura. U ovom radu je dan pregled primjene suvremenih metoda strojnog kao i dubokog učenja na problem određivanja interatomskih potencijala i sila. Osim toga, testiran je prijašnji strojno naučeni potencijal na strukturama sedmog slijepog testa predviđanja kristalnih struktura te su mu prijenosnim učenjem značajno popravljene performanse.
Abstract (english)	Machine learning has the ability to change the paradigm of calculating the properties of complex molecules and crystals. Previous precise methods based on density functional theory are computationally too expensive for complex tasks such as the prediction of crystal structures. This paper provides an overview of the application of modern methods of machine as well as deep learning to the problem of determining interatomic potentials and forces. In addition, the previous machine learning potential was tested on the structures of the seventh blind prediction test of crystal structures, and its performance was significantly improved by transfer learning.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:217:686126
Study programme	Title: 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 resource	Text
File origin	Born digital
Access conditions	Open access Embargo expiration date: 2024-07-15
Terms of use
Repository	Repository of the Faculty of Science
Created on	2022-09-07 09:24:33