# 3.7. Example: C16 and Ge16

Tip

The sample input and output files can be found in testfiles/atom/5-cge.

$$\mathrm{C}_{16}$$ and $$\mathrm{Ge}_{16}$$ are inorganic clusters with very delocalized electronic structures. In principle, one should use DFT to carry out global optimization. However, one can use Tersoff potential to do a first exploration. The parameters can be found in misc/atomic-force-field.txt:

misc/atomic-force-field.txt
C-C     1393.6   346.7    3.4879           2.2119      0.0000001524   0.72751 38049.0  4.384    -0.57058  1.8 2.1  1.0     1.0     # Phys. Rev. B 1989, 39, 5566
Si-Si   1830.8   471.18   2.4799           1.7322      0.0000011      0.78734 100390   16.217   -0.59825  2.7 3.0  1.0     1.0     # Phys. Rev. B 1988, 38, 9902


This suggests that these parameters are taken from the paper J. Phys. C 1985, 18, 1149. The parameters are ordered exactly the one that abcinp needs. Just simply copy them to abcinp.

For $$\mathrm{C}_{16}$$:

Step 1: call abcinp to generate input files:

abcinp c16 1 Tersoff 8 50 10000 5 10 16 C
Parameters for atom 0: A B lambda mu beta m c d h R S > 1393.6 346.7 3.4879 2.2119 0.0000001524 0.72751 38049.0 4.384 -0.57058 1.8 2.1
Parameters for atom-pair 0-0: kai omega > 1.0 1.0


Tip

You may find that the population size and maximum cycle number are very large. The reason is that, as mentioned in Theoretical Background, Tersoff potential is rather short-range (valence dependence), which is difficult for global optimization.

Step 2: Run the global optimization:

\$ atom c16.inp > c16.out


After a few seconds, you will find the global minimum in c16.xyz and local minima in c16-LM.

For $$\mathrm{Ge}_{16}$$, the global optimization is similar.

The global minima of $$\mathrm{C}_{16}$$ and $$\mathrm{Ge}_{16}$$ are shown below. They seem to be quite reasonable.