Implications of the BATTERY 2030+ AI‐Assisted Toolkit on Future Low‐TRL Battery Discoveries and Chemistries

Abstract BATTERY 2030+ targets the development of a chemistry neutral platform for accelerating the development of new sustainable high‐performance batteries. Here, a description is given of how the AI‐assisted toolkits and methodologies developed in BATTERY 2030+ can be transferred and applied to representative examples of future battery chemistries, materials, and concepts. This perspective highlights some of the main scientific and technological challenges facing emerging low‐technology readiness level (TRL) battery chemistries and concepts, and specifically how the AI‐assisted toolkit developed within BIG‐MAP and other BATTERY 2030+ projects can be applied to resolve these. The methodological perspectives and challenges in areas like predictive long time‐ and length‐scale simulations of multi‐species systems, dynamic processes at battery interfaces, deep learned multi‐scaling and explainable AI, as well as AI‐assisted materials characterization, self‐driving labs, closed‐loop optimization, and AI for advanced sensing and self‐healing are introduced. A description is given of tools and modules can be transferred to be applied to a select set of emerging low‐TRL battery chemistries and concepts covering multivalent anodes, metal‐sulfur/oxygen systems, non‐crystalline, nano‐structured and disordered systems, organic battery materials, and bulk vs. interface‐limited batteries.