While it is possible to exhaustively screen 42 substituents at seven positions, it will not be possible for future screening efforts that involve all nine positions and/or more than 42 substituents since the number of molecules will number in the trillions or more. We therefore test whether a genetic algorithm (GA) can find the best candidate in such a large data set efficiently, using the linear regression model since we know what the correct answer is. To screen for both high storage density and long half life, we train a linear regression model to predict the back reaction barrier height using same molecules we used to train the storage density model (Figure ). Since the model is only used to test the efficiency of GA searches, we trained one model (instead of two as is done for the storage The best candidate in the search space is defined as the molecule with the largest storage density and a barrier height ≥180 kJ/mol. From the exhaustive search we know that this molecule is 10 shown in Table with a storage density of 0.531 kJ/g. For the genetic algorithm each gene is defined as a vector with seven numbers (bases) ranging from 0 to 41, representing the seven possible positions and 42 possible substituents. The score for each gene is computed as