The individuals in each population need to be scored to assess how well they optimize a given property. The fitness function is a model that scores each individual and can range widely in complexity, from relatively simple tasks such as maximizing molecular weight that can be calculated simply with RDKit to more time-intensive tasks such as calculating the dipole moment with quantum-chemical tools like DFT. In most GAs, the fitness function is the time-limiting step, so care must be taken to use cost-effective calculations. One way to do this is to use computationally inexpensive techniques, such as semi-empirical methods like sTD-DFT-xTB for calculating optical bandgap instead of the more costly but accurate TD-DFT. Another common method is to utilize ML as the fitness function. For properties that can be either computationally time-intensive or too difficult to calculate, a pre-trained ML model can drastically speed up fitness evaluation time. One example of using ML as the fitness function in a GA is reported in our previous work , where we developed an ensemble random forest and neural network ML model to predict the power conversion efficiency (PCE) of materials for organic solar cells. This model was then used as the fitness function in a series of GAs to develop and find the best combination of new materials for tandem organic solar cells. Power conversion efficiency is arguably one of the most important metrics when developing solar cells, yet there is no simple computation to calculate it. It is highly dependent on multiple properties and ML is one of the most widely-used methods for computationally predicting it.