The new incremental strategy (hereafter denoted asnew) was compared to the spherical random reference generation (termedref). In the reference procedure the atoms are simply sorted in a sphere whose volume grows proportionally to the number of atoms. The main difference is that in the reference strategy the inclusion of an atom in the cluster was roughly independent of the atoms previously included although respecting the same validation parameters for proximity and stray atoms.
For validating the proposed strategy,500 generated clusters of the AlxMgx stoichiometry with12,16and 20atoms (Al6Mg6, Al8Mg8 and Al10Mg10) were analysed with respect to3 indicators:The cluster energy before any optimization step (raw energy),the cluster energy after L-BFGS optimization (optimized energy)andthenumber of iterationsperformed by the local optimization process (L-BFGS). The average values are shown inTable 2and their distributions are pictured in Figure 6.
Table 2 – Comparison betweennewandref average results.
Average number Raw energy Optimized energy
of iterations (eV) (eV)
Al6Mg6 new 131.944 -11.774245 -20.467526 ref 144.422 -8.389084 -20.477823 Al8Mg8 new 160.512 -16.153776 -28.310082 ref 165.752 -11.485943 -28.324609 Al10Mg10 new 174.744 -20.791567 -36.28177
ref 181.404 -14.639601 -36.25815
Figure 6 – Distribution of values for the500Al6Mg6clusters
20.8 20.6 20.4 20.2 20.0 19.8 19.6 19.4 19.2 opt_energies (eV)
Both incremental (proposed) and the reference approach were tested. The raw energy value of larger randomly generated clusters of the same stoichiometry with 40 and 60 atoms (Al20Mg20and Al30Mg30) were also analyzed for giving scalability insights for both strategies.
6.1.1 The raw energy.
The Raw energy values resembled a normal distribution with similar standard deviation, but with a shifted average as shown in the Table 3. The distributions of energy values were closely studied for AlxMgxstructures with12atoms shown inFigure 6comparing the indicators of the incremental strategy (new) and reference strategy (ref).
A hypothesis test was performed for evaluating the hypothesis of both distributions (raw and new) being samples of the same population for each indicator. α < 0.05 was considered a reasonable threshold for the rejection of that hypothesis. The tests considered the two samples to be independent of one another and using a scipy module for testing the null hypothesis that the 2 distributions could have identical average (expected) values.
Table 3 – Statistical tests result forref andnewraw energy values.
Average raw Standard
p-value energies (eV) deviation (eV)
Al6Mg6 new -11.774245 1.276208
<10−5
ref -8.389084 1.696443
Al8Mg8 new -16.153776 1.664292
<10−5 ref -11.485943 2.056415
Al10Mg10 new -20.791567 1.917559
<10−5 ref -14.639601 2.274293
The shift in the raw energies’ distribution at Figure 6a (also visible in Table 3) indicates a larger chance of generating a better structure before optimization using the proposed strategy (new). This gap is enforced by the hypothesis test result (p-value) shown inTable 3.
The significantly low p-value gives support to the conclusion that each approach has its distribution of raw energies (i. e. a different average and standard deviation). It is also believed that systems with more atoms tend to generate more isolated distributions.
This result encourages the search for further improvements in the generation strategy for being able to make GA runs operating over raw clusters and without frequent local relaxation.
Such an improvement could reduce the computational cost of the algorithm and provide better consistency for the GA operators and their learning process.
6.1.2 The optimized energy.
The distributions for the optimized energy values are shown in Figure 6b and inTable 4.
The histograms indicate that the optimized energies of both generation strategies follow the same distribution function instead of two dissociated distributions. This result is supported by the hypothesis test (p-value) shown inTable 4. The considerable value (greater than0.05) indicates that both approaches reach the optimal energy values with similar probability.
Table 4 – Comparison betweennewandref optimal energy values.
Average optimal Standard
p-value energies (eV) deviation (eV)
Al6Mg6 new -20.467526 0.259007
0.5352
ref -20.477823 0.265292
Al8Mg8 new -28.310082 0.331634
0.4992
ref -28.324609 0.347130
Al Mg new -36.281770 0.467075
0.4380
Figure 7presents a set of scatter plots for each individual matching the raw energy with the optimized energy showing that the frequency of achieving the global minimum is roughly the same between the proposed (new) and the reference strategy (ref) despite the proposed strategy scatter plots showing more clusters randomly generated with lower energy values.
It is noticeable inFigure 7that the clusters with the lowest raw energies do not necessarily generate the clusters with the lowest optimized energy. No correlation was found between raw and optimized energies for the clusters studied.
Figure 7 – Raw energy against optimized energy for the500AlxMgxclusters (a) Optimized x raw energies - Al6Mg6(new)
(a) Al.6_Mg.6 Optimized energies (new) (b) Optimized x raw energies - Al6Mg6(ref)
16 14 12 10 8 6 4 2
(b) Al.8_Mg.8 Optimized energies (new) (d) Optimized x raw energies - Al8Mg8(ref)
22 20 18 16 14 12 10 8 6
(e) Optimized x raw energies - Al10Mg10(new)
27.5 25.0 22.5 20.0 17.5 15.0 12.5 10.0 7.5
(c) Al.10_Mg.10 Optimized energies (new) (f) Optimized x raw energies - Al10Mg10(ref)
27.5 25.0 22.5 20.0 17.5 15.0 12.5 10.0 7.5
Another point of interest in this comparison is that the lowest optimized energies of Al6Mg6, shown in Figure 7a (new) and Figure 7b(ref), fall in disconnected plateaus indicating a level of discretization of the optimized structures, coherent with the results ofDoye, Miller &
Wales(1999) that showed the exponential growth of the number of minima with the size of the cluster.
6.1.3 The number of iterations.
The number of iterations taken by the L-BFGS method to relax the generated structure to a local minimum is relevant for a highly time-consuming objective function. In this case, the computational cost will be directly proportional to this number, and if one of the generating methods requires fewer iterations, it will be the most cost-effective. This values are shown in Table 5.
The results showed little difference between the two methods, approximately, from65to300 iterations in all distributions as shown inFigure 6c. The hypothesis test result shown by the p-value inTable 5indicated that the two approaches generated different average iterations being the new approach consistently (although moderately) more efficient.
Table 5 – Comparison betweennewandref number of iterations.
Average number Standard
p-value of iterations deviation (eV)
Al6Mg6 new 131.944 34.006247
<10−5 ref 144.422 37.238474
Al8Mg8 new 160.512 40.024366
0.0456 ref 165.752 42.621573
Al10Mg10 new 174.744 38.944043
0.0049 ref 181.404 35.542155
No correlation was found between the raw energy value and the number of iterations as show inFigure 8. In many cases, the cluster with worse raw energy required fewer iterations for the optimization. These results are credited to the complexity of the Hessian matrix, considering thate. g.Al10Mg10systems have54degrees of freedom.
Figure 8 – Raw energy against number of iterations for the500AlxMgxclusters
(a) Al.6_Mg.6 Number of iterations (new) (b) Iteratons x raw energies - Al6Mg6(ref)
16 14 12 10 8 6 4 2
(a) Al.6_Mg.6 Number of iterations (ref)
(c) Iteratons x raw energies - Al8Mg8(new)
(b) Al.8_Mg.8 Number of iterations (new) (d) Iteratons x raw energies - Al8Mg8(ref)
22 20 18 16 14 12 10 8 6
(b) Al.8_Mg.8 Number of iterations (ref)
(e) Iteratons x raw energies - Al10Mg10(new)
27.5 25.0 22.5 20.0 17.5 15.0 12.5 10.0 7.5
(c) Al.10_Mg.10 Number of iterations (new) (f) Iteratons x raw energies - Al10Mg10(ref)
27.5 25.0 22.5 20.0 17.5 15.0 12.5 10.0 7.5
(c) Al.10_Mg.10 Number of iterations (ref)