Computational modelling techniques are widely employed in

Research Proposal
by Robin Chang Yee Hui
Proposed Research Title
Material Design of III-Nitride Ternary via First Principles Calculations
Literature Review
Computational modeling techniques are widely employed in material science for the
past two decades due to progress in computing power and simulation methodologies.
Rapid decrease in the unit price of CPU power, physical memory and enormous hard
disk space for scientific calculations have found their way into the mass market.
There exist also at the same time commercially and non-commercially integrated
software packages such as Gaussian09, ABINIT, Wien2k, VASP, LAMMPS and
others. All these enables complex experiment to be carried out at relatively low cost,
thus it is a numerical experiment in disguise. At its best, using appropriate physical
models and the minimum pf ad hoc assumptions, computer simulations can be used
as a powerful tool to guide novel experiment programmes in a fraction of time and
cost of trial-and-error approaches. When being compared to experimental approach,
computational technique is relatively cheap and convenient, prevalently producing
interesting data and at times unearthing new materials. Computational modeling
virtually covered all areas of mainstream materials science, including polymers,
ceramics, semiconductors and metals. It is also a major driving force in new
avenues of research, such as pharmaceutical materials science [1], nanotechnology
and engineering [2], biomaterials [3], solid state electronics [4], to name a few.
An emerging paradigm for accelerated materials discovery is to design new
compounds in silicon using first principle calculations, and then perform experiments
on the computational designed candidates. Using a computational approach to help
design new materials offers several benefits that are complementary to traditional
experimental-based materials discovery. One key advantage of computations is the
level of control they offer compared to experiments. For example, it is often trivial in
calculations to simulate the effects of chemical substitutions of or lattice strain, but
achieving those same conditions experimentally could take many months of
painstaking laboratory work. In addition, characterizing a material’s fundamental
properties is often quicker with computations compared to experiment while still
retaining excellent or acceptable accuracy. This is most apparent in recent highthroughput DFT studies in which properties of thousands of materials have been
calculated in relatively short time frames, opening up the possibility for an
informatics-based approach to materials design [5].
A.R. Oganov, C.W. Glass [6] et. al. have developed an USPEX, a crystal structure
predictor code, by implementation of the evolutionary algorithm and many other
efficient variant operators. USPEX has gained success in various material structure
predictions. This can be seen in the paper published such as MgSiO3
postperovskite [7], unexpected sodium chlorides: Na3Cl, Na2Cl, Na3Cl2, NaCl3, NaCl7 [8],
low-energy two-dimensional boron structure [9], densest carbon material [10], and
super hard graphite [11], to name a few.
The group III nitrides (AlN, GaN and InN) represent semiconductors with direct band
gaps which span the range 1.95-6.2 eV. Their ability to generate efficient
electroluminescence has been the main driving force for their recent technological
High brightness visible light-emitting diodes (LEDs) are now
commercially available, a development which has transformed the market for
LED-based full colour displays and which has opened the way to many other
applications. On the other hand, III-nitride semiconductor ternary alloys such as
aluminum indium nitride AlxIn1-xN have attracted much research interest due to its
potential application in optoelectronic devise. AlxIn1-xN-based optoelectronic devices
such as the adjustable energy band gap InN and AlN, light emitted diode, laser diode
have been developed [12, 13]. AlxIn1-xN with 17-18% indium has also been used as
strain-free cladding layer on a GaN-based laser diode structure because Al xIn1-xN and
GaN are both lattice-matched [14]. This research will be an in-depth theoretical
study on wurtzite AlxIn1-xN alloys using USPEX. By varying the content of Aluminum
and thus Indium, we will let USPEX to “design” the optimum crystal structure through
density functional method search. Through these studies, a better understanding of
the electronic behavior in wurtzite Al xIn1-xN can be obtained and this will contribute to
the mankind knowledge in the condensed matter physics.
Research Objectives
To determine/design the optimum structure for each and every possible alloy
composition of AlxIn1-xN through USPEX, a crystal structure prediction module.
To determine the structural, electronic, optical and vibrational behaviors of the
optimum structure derived (through USPEX above) using designated density
functional (DFT) method.
To verify the theoretical investigation with the experimental data.
Planned procedures/methodology
Initially, deeper understanding of density functional theory (DFT), USPEX,
and the AlxIn1-xN must be gained (Literature Review). Al xIn1-xN (without
varying the composition of Al and In) will be constructed, relaxed and
optimized through dedicated DFT software. To familiarize with USPEX, result
from references [9] and [10] will be reproduced.
For each and every possible alloy composition of AlxIn1-xN, dedicated search
using USPEX will be carried. In USPEX, the optimum structure is searched
using VASP, a type of plane wave-based DFT software, in variable
composition mode. This will be done using computer clusters in the School of
Physics where MPI capability to run parallel codes are available.
After the structural search of AlxIn1-xN is done, it is followed by density
functional calculation (DFT) using the same DFT method in (2). Through
VASP, aforementioned properties of the AlxIn1-xN alloy can be obtained.
Finally, the results calculated from (2) and (3) above will be verified against
the experimental result which is currently in progress and work from our
experimental group in the School of Physics also.
J. A. Elliott and B. C. Hancock, Pharmaceutical materials science: an active
new frontier in materials research, Mater. Res. Soc. Bull., 31, 11, 869 (2006)
W. K. Liu, E. G. Karpov, S Zhang and H. S. Park, An introduction to
computational nanomechanics and materials, Comput. Methods Appl. Mech.
Eng., 193 (2004)
A. M. Stoneham, The challenges of nanostructures for theory, Mater Sci. Eng.
C, C23, (1-2), 235(2003).
S Kim., S. Yamaguchi and J. A. Elliott, Solid-state ionics in the 21 st century:
current status and future prospects., Mater. Res. Soc. Bull, 34, 12, 900 (2009)
G. Hunter, A Jain, S. P. Ong, From the computer to the laboratory: materials
discovery and design using first-principles calculations, J. Mater. Sci., 47, 21,
7317 (2012)
A.R. Oganov, C.W. Glass, Crystal structure prediction using evolutionary
algorithms: principles and applications. J. Chem. Phys. 124, art. 244704
A.R. Oganov, S. Ono, Theoretical and experimental evidence for a
post-perovskite phase of MgSiO3 in Earth's D" layer. Nature 430, 445-448
W.W. Zhang, A.R. Oganov, A.F. Goncharov, et al. Unexpected stoichiometries
of stable sodium chlorides. Science 342, 1502-1505 (2013)
X.F. Zhou, X. Dong, A.R. Oganov, Q. Zhu, Y. Tian and H.T. Wang,
Semimetallic two-dimensional boron allotrope with massless Dirac
fermions. Phys. Rev. Lett.112, 085502 (2014)
Q. Zhu, A.R. Oganov, M. Salvado, P. Pertierra, A.O. Lyakhov, Denser than
diamond:ab initio search for superdense carbon allotropes. Phys. Rev. B83,
193410 (2011)
Q. Li, Y. Ma, A.R. Oganov, H. Wang, Y. Xu, T. Cui, H.K. Mao and G. Zou,
Superhard monoclinic polymorph of carbon. Phys. Rev. Lett. 102, 175506
Carlin, J.F. et. al., Crack-free fully epitaxial nitride microactivity using highly
reflective AlIN/GaN Bragg mirrors, Appl. Phys. Lett., 86(3), 031107 (2005)
Guo Q. et. al, X-ray absorption near-edge fine structure study of AlInN
semiconductors, Appl. Phys. Lett., 86(11), 111911 (2005)
Butte R., Current status of AlInN layers lattice-matched to GaN for
photonics and electronics, J. of Physics D: Applied Physics, 40(20), 6328