Data analysis of multi-dimensional thermophysical properties of liquid substances based on clustering approach of machine learning

Gota Kikugawa, Yuta Nishimura, Koji Shimoyama, Taku Ohara, Tomonaga Okabe, Fumio S. Ohuchi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In order to develop an efficient framework for global screening in the material exploration, we performed a clustering analysis of machine learning on the multi-dimensional thermophysical properties of the liquid substances. Data mining using a self-organizing map (SOM)based on the unsupervised learning was employed to project high-dimensional thermophysical data onto a low-dimensional space. Here we adopted 98 liquid substances with eight thermo-physical properties for the SOM training in order to group the liquid substances. The present SOM-clustering approach properly categorized liquid substances according to the chemical species characterized by the functional groups.

Original languageEnglish
Pages (from-to)109-114
Number of pages6
JournalChemical Physics Letters
Volume728
DOIs
Publication statusPublished - 2019 Aug

Keywords

  • Clustering analysis
  • Heat medium
  • Machine learning
  • Self-organizing map
  • Thermophysical properties

ASJC Scopus subject areas

  • Physics and Astronomy(all)
  • Physical and Theoretical Chemistry

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