ION–RARE-GAS MICROSOLVATION STUDIES BY EMPLOYING AN EVOLUTIONARY ALGORITHM
Evolutionary Algorithm, Cluster Optimization, Microsolvation, DFT, Machine
Learning.
The microsolvation has been subject of intense theoretical and experimental study
with implications in diverse areas such as atmospheric chemistry, biological processes, per-
meation and transport membrane, among others. Therefore, this thesis aimed to develop a
study of the microsolvatation of ions through noble gases. One of our specific goals is to
survey the microsolvatation of ion Li+by argon, kripton and mixture of these atoms. For this
construction, analyses involving the interaction construction of two and three-bodies metal-
alkaline ions with noble gases, from the fitting of potential functions ab initios on the eletronic
energy using the methodology CCSD(T). The determination of stable clusters structures will
be given by the application of the evolutionary algorithm (EA). The evolutionary algorithm
will search of these low energy structures, both in global and local minima. In order to
strengthen this study, pos-optimization calculations using the ab initios CCSD(T) and MP2
methods are also performed. Treatment of clusters with noble gases of higher numbers, a
DFT methodology is also used in the stage of pos-optimization, with the goal to perform a
Benchmark analysis of these systems. Lastly, from diverse local minima generated by evo-
lutionary algorithm we applied a Machine Learning technique to enable the determination
of choose rules of the better EA minima that present an efficient description of the energy
landscape from the clusters length.