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The Evolutionary algorithm (EA), on the Atomic Simulation Environment (ASE), provides a means to find the lowest energy conformation molecule of a given stoichiometry. In this study we examine the ways in which the initial population of molecules affect the success of the EA. We have added a set of rules to the way in which the molecules are created that leads to more chemically relevant structures using chemical intuition. We have also implemented a clustering program that selects molecules that differ from each other from a large pool of molecules to form the initial population. Through testing of EA runs with and without clustering and intuitive population creation, the following success rates were obtained; no intuition and no clustering, 28±3%, no intuition with clustering, 31±4%, with fixed intuition but without clustering, 49±5%, with fixed intuition and clustering, 49±4%, with variable intuition and without clustering, 47±4%, and with variable intuition and clustering, 50±3%. A significant increase in success rate was found when implementing intuitive population creation while clustering the initial population seems to marginally help as the population becomes more diverse.

Improvement of the Evolutionary Algorithm on the Atomic Simulation Environment Though Intuitive Starting Population Creation and Clustering