Unfortunately, genetic algorithms are known to be vulnerable to evolutionary stagnation when encountering low-performing valleys or local optima . Enforcing diversity in the population of molecules a genetic algorithm uses can alleviate these issues. Quality-diversity algorithms , such as the graph-based elite patch illumination algorithm (GB-EPI), obtain this diversity by splitting the population into niches based on their physicochemical properties. In each generation, the best performing molecule in each of the individual niches is retained, rather than selecting the highest-scoring solutions regardless of their diversity.