There have been numerous reviews and conceptual papers about the high-level design of a MAP, ranging from concept papers , to building a common language to describe the advancement towards autonomous research 2 and the announcement of the Nobel Turing Challenge . The design of our MAP is motivated by a long-term vision of altering the way how we perform materials research , i.e. going beyond single-laboratory demonstrations and towards the creation of an ecosystem that enables atom-to-system innovation at global integration today. We believe that there is a fallacy in the current perception of the MAP paradigm. This misconception is in our view, that scientists often think of a single loop optimizing for a specific target. Battery research, or energy conversion research in a broader sense, is inherently multimodal and multitarget and therefore requires a truly modular MAP, going beyond a single research instrument, group and modality. Consequently, a MAP for energy conversion requires a setup combining hardware and software components that overcome boundaries between the scales through multiple experimental probes, simulators and data driven model building to exploit complementary capacities. In the process, it needs to minimize repetitive human actions while increasing the efficiency of experiments and maximizing knowledge generation in a time-and cost-efficient manner. Funding allocation efficiency requires better utilization of equipment, which we believe to be a defining characteristic of the MAP idea as existing equipment can be deployed in new innovative workflows with no additional effort. Emblematic for conventional optimization loops, that are not yet a MAP by our definition of a battery MAP, are demonstrations of optimizing fast charging capabilities of a specific battery chemistry, prolonging the lifetime of a cell chemistry under fast charging conditions and numerous others .