Soft Computing

, Volume 21, Issue 20, pp 6175–6182 | Cite as

Estimation of average surface energies of transition metal nitrides using computational intelligence technique

  • Taoreed Olakunle Owolabi
  • Kabiru Oluwaseun Akande
  • Sunday Olusanya Olatunji
Methodologies and Application
  • 146 Downloads

Abstract

Several properties of transition metal nitrides (TMN) that make them useful in many applications are closely related to the state of their surfaces. Meanwhile, high melting points which characterize these materials make the determination of their surface energies experimentally difficult. This work presents a computational intelligence technique using support vector regression (SVR) to establish, for the first time, a complete database of average surface energies of all members of TMN series. SVR-based model was developed by training and testing SVR with best parameters obtained through test-set–cross-validation technique using thirty-five experimental data of periodic metals. The developed SVR-based model was used to estimate average surface energies of 3d, 4d and 5d-TMN, and the obtained results agree well with the existing theoretical values. Simple and effective computational approach of the developed model together with its accurate estimation of average surface energies of all the members of TMN series contributes to the uniqueness of this developed model over the existing theoretical methods.

Keywords

Transition metal nitrides Support vector regression  Surface energy and descriptors 

Notes

Acknowledgments

We appreciate the reviewers of this manuscript for their constructive comments and suggestions that have improved the content of this manuscript considerably.

Compliance with ethical standards

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Taoreed Olakunle Owolabi
    • 1
    • 4
  • Kabiru Oluwaseun Akande
    • 2
  • Sunday Olusanya Olatunji
    • 3
  1. 1.Physics DepartmentKing Fahd University of Petroleum and MineralsDhahranKingdom of Saudi Arabia
  2. 2.Institute for Digital Communications, School of EngineeringUniversity of EdinburghEdinburghUK
  3. 3.Computer Science DepartmentUniversity of DammamDammamKingdom of Saudi Arabia
  4. 4.Physics and Electronics DepartmentAdekunle Ajasin UniversityAkungba-AkokoNigeria

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