Unsupervised machine learning algorithms as support tools in molecular dynamics simulations

Autores
Rim, Daniela; Moyano, Luis G.; Millán, Emmanuel N.
Año de publicación
2019
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.
Sociedad Argentina de Informática e Investigación Operativa
Materia
Ciencias Informáticas
Machine Learning
Unsupervised Algorithms
Molecular Dynamics
Granular Collisions
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/87939

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spelling Unsupervised machine learning algorithms as support tools in molecular dynamics simulationsRim, DanielaMoyano, Luis G.Millán, Emmanuel N.Ciencias InformáticasMachine LearningUnsupervised AlgorithmsMolecular DynamicsGranular CollisionsUnsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.Sociedad Argentina de Informática e Investigación Operativa2019-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf137-150http://sedici.unlp.edu.ar/handle/10915/87939enginfo:eu-repo/semantics/altIdentifier/issn/2451-7585info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/3.0/Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:17:33Zoai:sedici.unlp.edu.ar:10915/87939Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:17:33.814SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
title Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
spellingShingle Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
Rim, Daniela
Ciencias Informáticas
Machine Learning
Unsupervised Algorithms
Molecular Dynamics
Granular Collisions
title_short Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
title_full Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
title_fullStr Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
title_full_unstemmed Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
title_sort Unsupervised machine learning algorithms as support tools in molecular dynamics simulations
dc.creator.none.fl_str_mv Rim, Daniela
Moyano, Luis G.
Millán, Emmanuel N.
author Rim, Daniela
author_facet Rim, Daniela
Moyano, Luis G.
Millán, Emmanuel N.
author_role author
author2 Moyano, Luis G.
Millán, Emmanuel N.
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Machine Learning
Unsupervised Algorithms
Molecular Dynamics
Granular Collisions
topic Ciencias Informáticas
Machine Learning
Unsupervised Algorithms
Molecular Dynamics
Granular Collisions
dc.description.none.fl_txt_mv Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.
Sociedad Argentina de Informática e Investigación Operativa
description Unsupervised Machine Learning algorithms such as clustering offer convenient features for data analysis tasks. When combined with other tools like visualization software, the possibilities of automated analysis may be greatly enhanced. In the context of Molecular Dynamics simulations, in particular asymmetric granular collisions which typically consist of thousands of particles, it is key to distinguish the fragments in which the system is divided after a collision for classification purposes. In this work we explore the unsupervised Machine Learning algorithms k-means and AGNES to distinguish groups of particles in molecular dynamics simulations, with encouraging results according to performance metrics such as accuracy and precision. We also report computational times for each algorithm, where k-means results faster than AGNES. Finally, we delineate the integration of these type of algorithms with a well-known analysis and visualization tool widely used in the physics community.
publishDate 2019
dc.date.none.fl_str_mv 2019-09
dc.type.none.fl_str_mv info:eu-repo/semantics/conferenceObject
info:eu-repo/semantics/publishedVersion
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dc.language.none.fl_str_mv eng
language eng
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dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/3.0/
Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
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137-150
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instname_str Universidad Nacional de La Plata
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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