Scientists have developed a way of finding optimal peptide sequences: using a machine-learning algorithm as a collaborator. The algorithm analyzes experimental data and offers suggestions on the next best sequence to try, creating a back-and-forth selection process that reduces time needed to find the optimal peptide. The results could provide a new framework for experiments across materials science and chemistry.