Secondary batteries are a fascinating research topic, as findings on the lab scale have a realistic potential to impact the electrification of everything 1 . Therefore, great efforts are being undertaken across different domains, i.e., everything from ab initio calculations 2,3 on the atomic level to manufacturing improvements through defect detection . A European effort to bring together this multimodal and multidisciplinary research is the Battery2030+ 1 initiative, namely the battery interphase genome -materials acceleration platform 3 . The idea is to discover and optimize batteries by jointly bringing together theory and experiment in workflows that allow to seamless translation between the scales 7 through the utilization of machine learning 2 , artificial intelligence and lab automation 8 into a true materials acceleration platform . The established workflow to optimize systems such as batteries is, however, to look at isolated parts of the complex system i.e. to optimize electrolyte conductivity , reduce defect concentration on coated electrodes or to test whether or not an additive prolongs the cycle life of a battery . Sometimes, these methods rely on machine learning methods as the underlying physics are either not understood or very complex to model, or experiments are too costly. If there are, however, in principle, physical laws that connect complex data like UV-Vis absorption spectra and RGB images, then machine learning models work great as errors and uncertainties are low and manageable. More importantly are also benchmarks that enable scientists to both gamify research and break models in creative ways The optimization studies cited above broadly fall into research modes I-III and only hint at "the ultimate" research mode IV. One notable example for directly performing research mode IV is shown in Flores et al. that try to derive the laws governing electrolyte conductivity in complex solvation structures from data. In this manuscript, we seek to explore if we can engineer electrochemical experiments for generating maximally information-dense data or if there is the possibility to engineer a charging procedure that is more amendable to both machine learning studies and human interpretation than CCCV.