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dc.date.accessioned 2019-12-27T15:37:11Z
dc.date.available 2019-12-27T15:37:11Z
dc.identifier.uri http://sedici.unlp.edu.ar/handle/10915/87939
dc.description.abstract 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. en
dc.format.extent 137-150 es
dc.language en es
dc.subject Machine Learning es
dc.subject Unsupervised Algorithms es
dc.subject Molecular Dynamics es
dc.subject Granular Collisions es
dc.title Unsupervised machine learning algorithms as support tools in molecular dynamics simulations en
dc.type Objeto de conferencia es
sedici.identifier.issn 2451-7585 es
sedici.creator.person Rim, Daniela es
sedici.creator.person Moyano, Luis G. es
sedici.creator.person Millán, Emmanuel N. es
sedici.subject.materias Ciencias Informáticas es
sedici.description.fulltext true es
mods.originInfo.place Sociedad Argentina de Informática e Investigación Operativa es
sedici.subtype Objeto de conferencia es
sedici.rights.license Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)
sedici.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/
sedici.date.exposure 2019-09
sedici.relation.event XX Simposio Argentino de Inteligencia Artificial (ASAI 2019) - JAIIO 48 (Salta) es
sedici.description.peerReview peer-review es


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Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0) Except where otherwise noted, this item's license is described as Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported (CC BY-NC-SA 3.0)