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  • Simulations of molecular dynamics play an important role in computational chemistry and physics. Such simulations require accurate information about the state and properties of interacting systems. The computation of water cluster potential energy surface is a complex and computationally expensive operation. Therefore, machine learning methods such as Artificial Neural Networks have been recently employed to machine-learn and further approximate clusters potential energy surfaces. This works presents the application of another highly successful machine learning method, the Support Vector Regression, for the modeling and approximation of the potential energy of water clusters as representatives of more general molecular clusters.
  • Simulations of molecular dynamics play an important role in computational chemistry and physics. Such simulations require accurate information about the state and properties of interacting systems. The computation of water cluster potential energy surface is a complex and computationally expensive operation. Therefore, machine learning methods such as Artificial Neural Networks have been recently employed to machine-learn and further approximate clusters potential energy surfaces. This works presents the application of another highly successful machine learning method, the Support Vector Regression, for the modeling and approximation of the potential energy of water clusters as representatives of more general molecular clusters. (en)
Title
  • Towards the modeling of atomic and molecular clusters energy by support vector regression
  • Towards the modeling of atomic and molecular clusters energy by support vector regression (en)
skos:prefLabel
  • Towards the modeling of atomic and molecular clusters energy by support vector regression
  • Towards the modeling of atomic and molecular clusters energy by support vector regression (en)
skos:notation
  • RIV/61989100:27740/13:86088020!RIV14-MSM-27740___
http://linked.open...avai/riv/aktivita
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  • P(ED1.1.00/02.0070), P(EE.2.3.20.0073), P(EE2.3.30.0055), P(LM2011033), S
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  • 111400
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  • RIV/61989100:27740/13:86088020
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  • Water energy modeling; Support vector regression; Experiments (en)
http://linked.open.../riv/klicoveSlovo
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  • [94E37D0AAE70]
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  • Xi'an
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  • Danvers
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  • Proceedings - 5th International Conference on Intelligent Networking and Collaborative Systems, INCoS 2013
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  • Krömer, Pavel
  • Snášel, Václav
  • Stachoň, Martin
  • Vítek, Aleš
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number of pages
http://bibframe.org/vocab/doi
  • 10.1109/INCoS.2013.26
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  • IEEE
https://schema.org/isbn
  • 978-0-7695-4988-0
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  • 27740
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