Scientists have continuously innovated on the efficiency improvements of the research process over the last many decades . Automation of research tasks and their integration with data lineage tracking was one of the driving accelerators in the past 20 years in biotechnology and materials science . This research automation created the field of combinatorial materials science (CMS) that is utilizing the paradigm of well-defined composition and processing variation to unravel the underlying physicochemical relationships at a greater pace . The near endless breath of the chemical space does, however, render any brute force exploration for discovery futile. Effective research therefore necessitates approaches that can predict how to design materials according to a target functional property. This inverse design approach is rooted in the idea of rational design based descriptors that define composition-structure-property relationships. Recent advancements in research instrumentation and user interfaces have lowered the entrance barrier for high-throughput experimentation (HTE). Abundance of searchable data, enabled by data management , then sparked the proliferation of data-driven methods in synthesis , characterization , performance evaluation , and interpretation . The integration of accelerated research tasks in workflows guided by data driven methods was then conceived as the next evolutionary step in a 2018 workshop initiated by the UN Mission Innovation Initiative and called the "materials acceleration platform" (MAP) . To date, there have been early demonstrations of the MAP idea, but all have been constrained to a laboratory bench or building . Going beyond a single property or even transcending the laboratory is necessitated for a true battery map as there are multiple functional properties to optimize , e.g. lifetime, energy capacity, energy density, voltage range, cost, power density, safety, embodied energy, etc. All these critically depend on high-dimensional parameter spaces comprising the structure and processing of the materials as well as the assembly, composition and environmental conditions during operation of the cell system. The optimization of battery materials is therefore a truly multiscale challenge ranging from atoms to systems, nanoseconds to years and therefore cannot be mastered on a laboratory bench, a single institution, or domain alone. A complicating factor in all of this is the mutual exclusion of certain methods, i.e. density-functional theory (DFT) calculations cannot be run on the month scale and manufactured batteries cannot easily be analyzed by e.g. XPS. This is in stark contrast to all of the early MAP-inspired demonstrations from other fields , where the entire research lifecycle can be covered either using a single robotic setup or a single research group. A comprehensive MAP in battery research therefore needs to be distributed across different domains spanning the entire battery research value chain . Herein we discuss how we envision the design of a cooperative and resilient MAP that is spatially distributed across multiple countries and involves scientists from the experimental and modeling domain. Based on these MAP design considerations we deploy a demonstration to a team of scientists involved in the BIG-MAP project , where a brokering software system was hosted at the Karlsruhe Institute of Technology (KIT, Germany), laboratory experiments were performed at the Helmholtz Institute Ulm (HIU, Germany), computer simulations were performed at Dassault Systèmes (3DS, Germany, United Kingdom), the machine learning optimizer was developed and run at the Technical University of Denmark (DTU, Denmark), data and ontology interfaces were managed at DTU, Stiftelsen for industriell og teknisk forskning (SINTEF, Norway) and École polytechnique fédérale de Lausanne (EPFL, Switzerland). This workflow is capable of determining the density, viscosity, and pair distribution functions and a machine learning based prediction of conductivity of aprotic electrolytes for Li-ion batteries. To the best of our knowledge, this is the first demonstration of an international MAP. After intensive design discussion, prior to the actual run, the real-world deployment of a MAP involving many partners was a valuable learning experience which we discuss at the end of this manuscript. We believe that sharing not just the success but also the failures of this new paradigm of performing research will help the community in avoiding some of the errors we made and accelerate the progress towards true autonomy.