Committee machine that votes for similarity between materials

Duong Nguyen Nguyen, Tien Lam Pham, Viet Cuong Nguyen, Tuan Dung Ho, Truyen Tran, Keisuke Takahashi, Hieu Chi Dam

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


A method has been developed to measure the similarity between materials, focusing on specific physical properties. The information obtained can be utilized to understand the underlying mechanisms and support the prediction of the physical properties of materials. The method consists of three steps: Variable evaluation based on nonlinear regression, regression-based clustering, and similarity measurement with a committee machine constructed from the clustering results. Three data sets of well characterized crystalline materials represented by critical atomic predicting variables are used as test beds. Herein, the focus is on the formation energy, lattice parameter and Curie temperature of the examined materials. Based on the information obtained on the similarities between the materials, a hierarchical clustering technique is applied to learn the cluster structures of the materials that facilitate interpretation of the mechanism, and an improvement in the regression models is introduced to predict the physical properties of the materials. The experiments show that rational and meaningful group structures can be obtained and that the prediction accuracy of the materials' physical properties can be significantly increased, confirming the rationality of the proposed similarity measure.

Original languageEnglish
Pages (from-to)830-840
Number of pages11
Publication statusPublished - 2018
Externally publishedYes


  • data mining
  • first-principles calculations
  • machine learning
  • materials informatics
  • physical properties of materials
  • similarity

ASJC Scopus subject areas

  • Chemistry(all)
  • Biochemistry
  • Materials Science(all)
  • Condensed Matter Physics


Dive into the research topics of 'Committee machine that votes for similarity between materials'. Together they form a unique fingerprint.

Cite this