Estimation of average surface energies of transition metal nitrides using computational intelligence technique
- 146 Downloads
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.
KeywordsTransition metal nitrides Support vector regression Surface energy and descriptors
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.
- Abajingin DD (2012) Solution of morse potential for face centre cube using embedded atom method. Adv. Phys. Theor. Appl. 8:36–45Google Scholar
- Adewumi AA, Owolabi TO, Alade IO, Olatunji SO (2016) Estimation of physical, mechanical and hydrological properties of permeable concrete using computational intelligence approach. Appl. Soft Comput. J. 2015:1–9Google Scholar
- Basak D, Pal S, Partababis DC (2007) Support vector regression. Neural Inf. Process. 11:203–224Google Scholar
- Guillermet F (1991) Band structure and cohesive properties of 3d-transition-metal carbides and nitrides with the NaCl-type structure. Phys. Rev. B 43(18):400–408Google Scholar
- Gupta SM (2007) Support vector machines based modelling of concrete strength. World Acad. Sci. Eng. Technol. 36:305–311Google Scholar
- Igarashi VVM, Khantha M (1991) N-body interatomic potentials for hexagonal packed metal. Philos. Mag. part B 63(3):1–10Google Scholar
- Kittel C (1976) Introduction to solid state physics. Wiley, p 20Google Scholar
- Ni AK, de Boer FR, Boom R, Mattens WCM, Miedema AR (1988) Cohesion in metals. North-Holland, AmsterdamGoogle Scholar
- Savino EJ (1992) Embedded-atom-method interatomic potentials for hcp metals. Phys. Rev. B 45(22):704–710Google Scholar
- Zhang H, Zhang Y, Dai D, Cao M, Shen W (2015) Modelling and optimization of the superconducting transition temperature. Mater. Des. 92(2016):371–377Google Scholar