The ability to understand and control battery interfaces and interphases is essential for the development of ultra-performing, smart and sustainable batteries. The chemical space within a battery is comprised of a multitude of different elements and structures that cross influence each other. The chemical composition of electrodes, formulation of electrolytes, electrode manufacturing process, packaging, and cell aging are all examples of this. The combinatorics of this space is enormous and exhaustive to explore in the lab – today’s existing methodologies are simply not enough if we want to accelerate the battery discovery process.

BIG-MAP, the largest of the BATTERY 2030+ projects, consists of two parts – BIG and MAP. BIG stands for Battery Interface Genome and MAP for Materials Acceleration Platform. The projects will develop physics-aware machine and deep learning models that can efficiently utilize petabytes of training data to establish the Battery Interface Genome (BIG), and predict how battery materials and interfaces evolve in space and time. The aim is to create a cost-effective path to fast track inverse design of future battery materials and technologies, based on a profound understanding of the chemical and physical properties in battery systems.