Molecular dynamics of charged dipole particles using machine learning

Kukhar, Oleksandr

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Students Research & Project Works | Роботи студентів
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Факультет прикладних наук
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Бакалаврська програма "Комп'ютерні науки"
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2023
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dc.contributor.author	Kukhar, Oleksandr
dc.date.accessioned	2024-02-14T09:11:31Z
dc.date.available	2024-02-14T09:11:31Z
dc.date.issued	2023
dc.identifier.citation	Kukhar, Oleksandr. Molecular dynamics of charged dipole particles using machine learning/ Oleksandr Kukhar; Supervisor: Taras Patsahan; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2023. – 31 p.: ill.	uk
dc.identifier.citation	Kukhar, Oleksandr. Molecular dynamics of charged dipole particles using machine learning / Oleksandr Kukhar; Supervisor: Taras Patsahan; Ukrainian Catholic University, Department of Computer Sciences. – Lviv: 2023. – 31 p.: ill.
dc.identifier.uri	https://er.ucu.edu.ua/handle/1/4408
dc.language.iso	en	uk
dc.title	Molecular dynamics of charged dipole particles using machine learning	uk
dc.type	Preprint	uk
dc.status	Публікується вперше	uk
dc.description.abstracten	The method of molecular dynamics finds applications in various areas such as pharmacology, polymer science, nanotechnology, chemical catalysis, and drug dis- covery. An efficient and fast prediction of positions and dynamics of particles is of great importance in order to reduce computational efforts. This thesis focuses on extending the existing SE(3)-transformer-based graph neural network (GNN) approach proposed by Fuchs et al.[1], which successfully employs a self-attention mechanism for point clouds to describe dynamics of charged particles. The exten- sion developed in our study is aimed to improve an accuracy of molecular dynamics prediction for a more complex system consisting of particles with an orientation- dependent interaction and rotational degrees of freedom. As an example, a physical model presented as a fluid of charged particles bearing electric dipoles is examined. It is shown that our approach, which introduces a new attention mechanism, pro- vides better accuracy in describing such systems compared to the original approach.	uk