Predicting cation ordering in MgAl₂O₄ using genetic algorithms and density functional theory

References

• Gale, J. Gulp: A computer program for the symmetry-adapted simulation of solids. Journal of the Chemical Society, Faraday Transactions 1997, 93, 629.
   Google Scholar
• Oganov, A.; Glass, C.W. How to predict very large and complex crystal structure. The Journal of Chemical Physics 2006, 124, 244704.
   PubMed Web of Science ®Google Scholar
• Revard, B.C.; Tipton, W.W.; Hennig, R.G. How to predict very large and complex crystal structure. Topics in Current Chemistry 2014, 345, 181–222.
   PubMed Web of Science ®Google Scholar
• Lonie, D.C.; Zurek, E. Xtalopt: An open-source evolutionary algorithm for crystal structure prediction. Computer Physics Communications 2011, 182, 372–387.
   Web of Science ®Google Scholar
• Devi, R.V.; Sithya, S.S.; Coumar, M.S. Evolutionary algorithms for de nove drug design – a survey. Applied Soft Computing 2014, 27, 543–552.
   Web of Science ®Google Scholar
• Le, T.C.; Winkler, D.A. Discovery and optimisation of materials using evolutionary approaches. Chemical Reviews 2016, 116, 6107–6132.
   PubMed Web of Science ®Google Scholar
• Wang, Y.; Ma, Y. Perspective: Crystal structure prediction at high pressure. The Journal of Chemical Physics 2014, 140, 040901.
   PubMed Web of Science ®Google Scholar
• Woodley, S.M.; Catlow, R. Crystal structure prediction from first principles. Nature Materials 2008, 7, 937–946.
   PubMed Web of Science ®Google Scholar
• Neto, V.; Paranaiba, O. Intelligent computational nanotechnology: The role of computational intelligence in the development of nanoscience and nanotechnology. Journal of Computational and Theoretical Nanoscience 2014, 11, 928–944.
   Google Scholar
• Chakraborti, N. Genetic algorithms in materials design and processing. International Materials Reviews 2004, 49, 246.
   Web of Science ®Google Scholar
• Paszkowicz, W. Genetic algorithms, a nature-inspired tool: Survey of applications in materials science and related fields. Materials and Manufacturing Processes 2009, 24, 174.
   Web of Science ®Google Scholar
• Bhoskar, T.; Kulkarni, O.K.; Kulkarni, N.K.; Patekar, S.L.; Kakandikar, G.M.; Nandedkar, V.M. Genetic algorithm and its applications to mechanical engineering: A review. Materials Today: Proceedings 2015, 2, 2624–2630.
   Google Scholar
• Lehmann, C.W. Crystal structure prediction – Dawn of a new era. Angewandte Chemie 2011, 50, 5616.
   Google Scholar
• Day, G.; Görbitz, C.H. Introduction to the special issue on crystal structure prediction. Acta Crystallographica 2016, B72, 435–436.
   Google Scholar
• Kolmogorov, A.N.; Shah, S.; Margine, E.R.; Bialon, A.F.; Hammerschmidt, T.; Drautz, R. New superconducting and semiconducting Fe-B compounds prediction with an Ab initio evolutionary search. Physical Review Letters 2010, 105, 217003.
   PubMed Web of Science ®Google Scholar
• Lee, I.-H.; Oh, Y.J.; Kim, S.; Lee, J.; Chang, K.J. Ab initio materials design using conformational space annealing and its application to searching for direct band gap silicon crystals. Computer Physics Communications 2016, 203, 110–121.
   Web of Science ®Google Scholar
• Mohn, C.E.; Kob, W. A genetic algorithm for the atomistic design and global optimisation of substitutionally disordered materials. Computational Materials Science 2009, 45, 111.
   Web of Science ®Google Scholar
• Mohn, C.E.; Kob, W. Predicting the structure of alloys using genetic algorithms. Materials and Manufacturing Processes 2011, 26, 348–353.
   Web of Science ®Google Scholar
• Mohn, C.E.; Kob, W. Predicting complex mineral structures using genetic algorithms. Journal of Physics: Condensed Matter 2015, 27, 425201.
   PubMed Web of Science ®Google Scholar
• Schultz, P.; Simon, A.; Oekler, O. Cation ordering in mangesioferrite MgFe2O4. Acta Crystallographica Section B 2016, B72.
   Google Scholar
• Schön, J.C.; Jansen, M. Determination, prediction and understanding of structures, using the energy landscapes of chemical systems. Zeitschrift für Kristallographie 2001, 216, 307.
   Google Scholar
• Mohn, C.E.; Trønnes, R. Iron spin state and site distribution in FeAlO3-bearing bridgmanite. Earth and Planetary Science Letters 2016, 440, 178.
   Web of Science ®Google Scholar
• Tealdi, C.; Lavrentiev, M.Y.; Mohn, C.E.; Allan, N.L. Perovskite solid solutions-a Monte Carlo study of the deep earth analogue (K, Na)MgF3. The Journal of Structural Chemistry 2016, 57, 257–266.
   Web of Science ®Google Scholar
• Mohn, C.E.; Stølen, S. Genetic mapping of the distribution of minima on the potential energy surface of disordered systems. The Journal of Chemical Physics 2005, 123, 114104.
   PubMed Web of Science ®Google Scholar
• Smith, R.W. Energy minimization of binary alloy models via genetic algorithm. Computer Physics Communications 1992, 71, 134.
   Web of Science ®Google Scholar
• Van Hoyweghen, C.; Goldberg, D.E.; Naudts, B. Spin-flip symmetry and synchronization. Evolutionary Computation 2002, 10, 317.
   PubMed Web of Science ®Google Scholar
• Bush, T.S.; Gale, J.D.; Catlow, C. R. A.; Battle, P.D. Self-consistent interatomic potentials for the simulation of binary and ternary Oxides. Journal of Materials Chemistry 1994, 4, 831–837.
   Web of Science ®Google Scholar
• Lewis, G.V.; Catlow, C. R. A. Potential models for ionic solids. Journal of Physics C: Solid State Physics 1985, 18, 1149–1161.
   Web of Science ®Google Scholar
• Lavrentiev, M.Y.; Purton, J.A.; Allan, N.L. Ordering in spinels – a Monte Carlo study. American Mineralogist 2003, 88, 1522–1531.
   Web of Science ®Google Scholar
• Perdew, J.P.; Burke, K.; Ernzerhof, M. Generalized gradient approximation made simple. Physical Review Letters 1996, 77, 3865–3868.
   PubMed Web of Science ®Google Scholar
• Kresse, G.; Joubert, J. From ultrasoft pseudopotentials to the projector augmented-wave method. Physical Review B 1999, 59, 1758–1775.
   Web of Science ®Google Scholar
• Woodley, S.M.; Battle, P.D.; Gale, J.D.; Catlow, C. R. A. The prediction of inorganic crystal structures using a genetic algorithm and energy minimisation. Physical Chemistry Chemical Physics 1999, 1, 2535–2542.
   Web of Science ®Google Scholar
• Dick, B.G.; Overhauser, A.W. Theory of the dielectric constants of alkali halide crystals. Physical Review 1958, 112, 90–103.
   Web of Science ®Google Scholar
• Andreozzi, G.B.; Princivalle, F.; Skogby, H.; Della Giusta, A. Cation ordering and structural variations with temperature in MgAl2O4 spinel: An X-ray single-crystal study. American Mineralogist 2000, 85, 1164–1171.
   Web of Science ®Google Scholar
• Seko, A.; Yuage, K.; Oba, F.; Kuwabara, A.; Tanaka, I.; Yamamoto, T. First-principles study of cation disordering in MgAl2O4 spinel with cluster expansion and Monte Carlo simulation. Physical Review B 2006, 73, 094116.
   Web of Science ®Google Scholar
• Méducin, F.; Redfern, S. A. T.; Godec, T.L.; Stone, H.J.; Tucker, M.G.; Dove, M.T.; Marshall, W.G. Study of cation order-disorder in MgAl2O4 spinel in situ neutron diffraction up to 1600 K and 3.2 GPa. American Mineralogist 1999, 84, 905–912.
   Web of Science ®Google Scholar
• Pavese, A.; Artioli, G.; Hull, S. In situ neutron powder diffraction of cation partitioning vs. pressure in Mg0.94Al0.06O4 synthetic spinel. American Mineralogist 1999, 84, 905–912.
   Web of Science ®Google Scholar