Download Computational Discovery of Two-Dimensional Materials with a

Computational Discovery of Two-Dimensional Materials
with a Genetic Algorithm for Structure Prediction
Benjamin Revard1,2, Arunima Singh3, Rohit Ramanathan1,
Michael Ashton2, Richard G. Hennig1,2
1
Department of Materials Science and Engineering, Cornell University, Ithaca, NY, 14850, U.S.A.
Department of Materials Science and Engineering, University of Florida, Gainesville, FL 32611, U.S.A.
3
Materials Science and Engineering Division, National Institute of Standards and Technology, Gaithersburg, MD 20899, U.S.A.
2
Enthalpy (eV/atom)
Materials exhibiting periodicity in fewer than three dimensions have become increasingly
important in modern technology due to their unique properties. Low-dimensional
materials can exhibit unexpected structures, and in some cases, the preferred crystal
structure of a low-dimensional material is quite different from the structure of its bulk
counterpart. To address the problem of predicting the crystal structures of lowdimensional materials, we have developed a grand-canonical genetic algorithm for
(a) 2D Sn-O System
(b) 2D Pb-O
System capable of searching
structure
prediction
for
(i)
0.2 structures with periodicity in zero,
one,(i)two and three dimensions [1]. We
0.0
0.0
apply the algorithm, coupled with
−0.2
density-functional methods, to search for
−0.5
single-layer materials in the InP, Sn-S
−0.4
and C-Si systems [1], and also in the
−0.6
group-IV dioxides AO2 (A = Si, Ge, Sn,
−1.0
−0.8
(iii) several
Pb) [2]. Our searches uncover
(ii)
novel 2D2Dstructures
of InP, as well as
−1.0
2D GA
GA
−1.5
(iii)
low-energy
Si
defects
in graphene and
Bulk
Bulk
(ii)
−1.2
new
2D 0.2
group-IV
dioxide
materials.
In1.0
0.0
0.2
0.4
0.6
0.8
1.0
0.0
0.4
0.6
0.8
we
find
GeO
to
exhibit
2
Sn
O particular,
Ofraction
Ofra
ction
Pb
O
Figure 1. Predicted phase diagram of 2D Sn-O system. several nearly degenerate 2D phases with
low
formation
energies.
We
computationally characterize the dynamic and environmental stability and electronic
structure of these new 2D group-IV dioxide materials and reveal them to be promising
candidates for gate oxides in nanoelectronic devices.
References:
[1] Revard, B. C., Tipton, W. W., Yesypenko, A., & Hennig, R. G. (2016). Grand-canonical evolutionary
algorithm for the prediction of two-dimensional materials. Physical Review B, 93(5), 054117.
[2] Singh, A. K., Revard, B. C., Ramanathan, R., Ashton, M., Tavazza, F., & Hennig, R. G. (2017). Genetic
algorithm prediction of two-dimensional group-IV dioxides for dielectrics. In print.