Machine learning assisted determination of linearly independent set of generalized molecular coordinates

Ostojić, Tea

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master's thesis

Machine learning assisted determination of linearly independent set of generalized molecular coordinates

Zagreb: University of Zagreb, Faculty of Science, 2021. urn:nbn:hr:217:138103

Ostojić, Tea

University of Zagreb
Faculty of Science
Department of Chemistry

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APA 6th Edition

Ostojić, T. (2021). Machine learning assisted determination of linearly independent set of generalized molecular coordinates (Master's thesis). Zagreb: University of Zagreb, Faculty of Science. Retrieved from https://urn.nsk.hr/urn:nbn:hr:217:138103

MLA 8th Edition

Ostojić, Tea. "Machine learning assisted determination of linearly independent set of generalized molecular coordinates." Master's thesis, University of Zagreb, Faculty of Science, 2021. https://urn.nsk.hr/urn:nbn:hr:217:138103

Chicago 17th Edition

Harvard

Ostojić, T. (2021). 'Machine learning assisted determination of linearly independent set of generalized molecular coordinates', Master's thesis, University of Zagreb, Faculty of Science, accessed 21 March 2025, https://urn.nsk.hr/urn:nbn:hr:217:138103

Vancouver

Ostojić T. Machine learning assisted determination of linearly independent set of generalized molecular coordinates [Master's thesis]. Zagreb: University of Zagreb, Faculty of Science; 2021 [cited 2025 March 21] Available at: https://urn.nsk.hr/urn:nbn:hr:217:138103

IEEE

T. Ostojić, "Machine learning assisted determination of linearly independent set of generalized molecular coordinates", Master's thesis, University of Zagreb, Faculty of Science, Zagreb, 2021. Available at: https://urn.nsk.hr/urn:nbn:hr:217:138103

Cite this item: https://urn.nsk.hr/urn:nbn:hr:217:138103

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Title	Machine learning assisted determination of linearly independent set of generalized molecular coordinates
Title (croatian)	Određivanje linearno nezavisnog skupa generaliziranih molekularnih koordinata primjenom strojnog učenja
Author	Tea Ostojić
Mentor	Tomica Hrenar (mentor)
Committee member	Tomica Hrenar (predsjednik povjerenstva)
Committee member	Ivan Kodrin (član povjerenstva)
Committee member	Željka Soldin (član povjerenstva)
Committee member	Branimir Bertoša (član povjerenstva)
Granter	University of Zagreb Faculty of Science (Department of Chemistry) Zagreb
Defense date and country	2021-05-18, Croatia
Scientific / art field, discipline and subdiscipline	NATURAL SCIENCES Chemistry
Abstract	Trajectory analysis can provide information on the complete conformational or configurational space of molecular systems and their reactivity. Numerical analysis of trajectories can be performed in any type of coordinate system, e.g. Cartesian coordinate system, internal or normal coordinates, etc. In some applications, various coordinate systems will hold redundant information due to the linear dependence. A set of molecular coordinates contains null-vectors that introduce numerical instabilities and noise in the processing. In the case of a larger number of defined coordinates, there is also a problem with storage and data processing. In this work, the procedure for constructing generalized and linearly independent set of internal molecular coordinates will be evaluated. Generalized internal coordinates are chemically intuitive and can be defined in a number of ways, for example: distances, angles or dihedral angles. By applying machine learning to molecular dynamics trajectory, a complete and linearly dependent set of generalized internal coordinate distances will be reduced to the linearly independent set that still contains all relevant information about conformational or configurational space, reactivity and molecular motion. Given procedure will be applied to the molecule of (R)-cinchonidine.
Abstract (croatian)	Analiza trajektorije može pružiti informacije o potpunom konformacijskom ili konfiguracijskom prostoru molekularnih sustava i njihovoj reaktivnosti. Numerička analiza trajektorija može se provesti u bilo kojem koordinatnom sustavu, npr. Cartesiusov koordinatni sustav, sustav internih ili normalnih koordinata, itd. No, u nekim aplikacijama različiti koordinatni sustavi zadržavaju suvišne informacije zbog linearne zavisnosti. Taj skup molekularnih koordinata sadrži nul-vektore koji onda uzrokuju numeričke nestabilnosti i numerički šum. U slučaju većeg broja definiranih koordinata također postoji i problem s pohranom te obradom podataka. U ovom će se radu razraditi postupak za kreiranje generaliziranog i linearno nezavisnog skupa internih koordinata. Generalizirane interne koordinate kemijski su intuitivne, a mogu se definirati na više načina, na primjer: međuatomne udaljenosti, kutovi ili diedarski kutovi. Primjenom strojnog učenja na trajektoriju molekularne dinamike, potpun i linearno zavisan skup generaliziranih internih koordinata međuatomnih udaljenosti reducirat će se na linearno nezavisan skup, ali skup koji još uvijek sadrži sve relevantne informacije o konformacijskom ili konfiguracijskom prostoru, reaktivnosti i gibanju molekula. Razrađeni postupak primijenit će se na molekulu (R)-cinhonidina.
Keywords
Keywords (english)
Language	english
URN:NBN	urn:nbn:hr:217:138103
Project	Number: IP-2016-06-3775 Title: Aktivnošću i in silico usmjeren dizajn malih bioaktivnih molekula Title: Activity and in silico guided design of bioactive small molecules Acronym: ADESIRE Leader: Tomica Hrenar Jurisdiction: Croatia Funder: HRZZ Funding stream: IP
Study programme	Title: Graduate university studies in chemistry; specializations in: Research programme Course: Research programme Study programme type: university Study level: graduate Academic / professional title: magistar/magistra kemije (magistar/magistra kemije)
Type of resource	Text
File origin	Born digital
Access conditions	Open access Embargo expiration date: 2023-05-18
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Repository	Repository of the Faculty of Science
Created on	2021-05-28 12:40:46