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ISCS 2011 Selected Papers Vol.2 Crystal Structure Prediction Using Evolutionary Algorithm 4.2 Phase Transition Simulation Result of generation series for each pressure is similar to single phase simulation. The reoptimized last stablest generation for all pressures are summarized in Table 4, and we also found metastable structures for some pressures. Table 4: Summary of the reoptimized last generation of all pressures in phase transition simulation. Press.(GPa) Struct.[S] Vol.(˚A 3 )[S] H(eV)[S] Struct.[M] Vol.(˚A 3 )[M] H(eV)[M] 95 SC 10.1469 -237.29666 100 SC 10.0380 -236.98075 110 SC 9.8366 -236.35717 SH 9.2032 -236.28198 120 SC 9.6456 -235.74231 SH 9.0181 -235.71340 130 SC 9.4509 -235.15132 SH 8.8409 -235.15565 140 SC 9.2821 -234.56238 d-SC 8.9916 -234.55954 150 SH 8.5704 -234.04512 SC 9.1134 -233.99563 160 SH 8.4116 -233.53843 SC 8.9842 -233.42351 170 SH 8.2897 -233.01649 SC 8.8512 -232.86837 180 SH 8.1739 -232.50200 SC 8.7302 -232.31919 190 SH 8.0660 -232.00137 SC = Simple Cubic; SH = simple Hexagonal; d-SC = Distorted Simple Cubic (a) (b) Figure 7: Profiles of (a) volume versus pressure and (b) enthalpy versus pressure. From Table 4, we ploted a graph of volume versus pressure and enthalpy versus pressure, as depicted in Figure 7, with the curve of Murnaghan equation of state in which its parameters are taken from the works of Akahama et al. [13, 14]. It is shown in Figure 7 that the transition pressure obtained from our simulation occurs at around 140 GPa. It is overestimated, compared to the experimental result (137 GPa) of Akahama et al. [13]. Our prediction volumes at the pressures above 150 GPa are also lower than the one predicted by the experiment (Murnaghan state of equation). This underestimation is probably due to the k-point mesh which should be finer 43
ISCS 2011 Selected Papers Vol.2 A. M. Hanna, T. Yoshizaki, M. A. Martoprawiro, T. Oda at high-pressure condition or the typical accuracy of GGA approach. The pressure dependences of the volume and enthalpy are consistent with a typical behavior at the first-order phase transition. Around the transition point, the lattice type is switched over between simple cubic and simple hexagonal structures for both of the ground state structure and the metastable one. 5 Conclusion We have performed simulations of crystal structure prediction using the evolutionary algorithm method in combination with ab-initio calculation. Results of our simulation have a good agreement with the previous experimental works. We found that this implementation of evolutionary algorithm method on crystal structure prediction formed a good new path to answer Maddox’s challenge of the crystal structure prediction. We also found the metastable structure was switched in accordance with the transition pressure. The evolutionary algorithm simulation experienced in this work strongly supports us to the research of crystal structure predictions. References [1] J. Maddox. (1988). Crystal from first principles. Nature, 335(6187), 210. [2] G. Trimarchi and A. Zunger. (2007). Global space-group optimization problem: Finding the stablest crystal structure without constraints. Phys. Rev. B, 75, 104113-1–104113-8. [3] P.F. McMillan. (2002). New materials from high-pressure experiments. Nat. Mater., 1(1), 19-25. [4] A. R. Oganov and C. W. Glass. (2006). Crystal structure prediction using ab initio evolutionary techniques J. Chem. Phys., 124(24), 244704. [5] S.M. Woodley, and C.R.A. Catlow. (2009). Structure prediction of titania phases. Computational Materials Science, 45(1), 84–95. [6] A. R. Oganov, Y. Ma, A. O. Lyakhov, M. Valle, and C. Gatti. (2010). Evolutionary crystal structure prediction as a method for the discovery of minerals and materials Reviews in Mineralogy and Geochemistry, 71(1), 271–298. [7] C. W. Glass, A. R. Oganov and N. Hansen. (2006). USPEX-evolutionary crystal structure prediction Computer Physics Communications, 175(11-12), 713–720. [8] P. Hohenberg and W. Kohn. (1964). Inhomogeneous electron gas Phys. Rev. 136, B864-B871.; W. Kohn and L. J. Sham. (1965). Self-consistent equations including exchange and correlation effects Phys. Rev. 140, A1133–A1138. [9] D. Vanderbilt (1990). Soft self-consistent pseudopotentials in a generalized eigenvalue formalism Phys. Rev. B, 41, 7892-7895. [10] J. P. Perdew, J. A. Chevary, M. R. Pederson, D. J. Singh, and C. Fiolhais. (1992). Atoms, molecules, solids, and surfaces: Applications of the generalized gradient approximation for exchange and correlation, Phys. Rev. B, 46, 6671-6687. [11] A.R. Oganov and M. Valle. (2009). How to quantify energy landscapes of solids. J. Chem. Phys., 130(10), 104504. 44
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