Evolutionary algorithms can combat these difficulties by requiring zero prior knowledge and guiding the search through chemical space. Genetic algorithms (GA) are a promising technique, mimicking natural processes such as the theory of natural selection . Using a random initial population of molecules, they undergo selection, crossover, and mutation operations to build a stronger subsequent population. In each generation after this cycle, the molecules are evaluated using a fitness function to predict their performance. Previous work in our group used these algorithms to design organic photovoltaic materials and infer new design rules . Our most recent study designed unfused NFAs and the GA revealed interesting strategies such as the inclusion of vinylene bridges and broken conjugation backbones . With this evolutionary approach, chemical space can be searched quickly and efficiently.