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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/87939
Ver los metadatos del registro completo
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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 Objeto de conferencia http://purl.org/coar/resource_type/c_5794 info:ar-repo/semantics/documentoDeConferencia |
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conferenceObject |
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publishedVersion |
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http://sedici.unlp.edu.ar/handle/10915/87939 |
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dc.language.none.fl_str_mv |
eng |
language |
eng |
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info:eu-repo/semantics/altIdentifier/issn/2451-7585 |
dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess 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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openAccess |
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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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