![]() By embedding multisensory and self-healing capabilities in future battery technologies and integrating these with AI and physics-aware machine learning models capable of predicting the spatio-temporal evolution of battery materials and interfaces, it will, in time, be possible to identify, predict and prevent potential degradation and failure modes. Creating a holistic, closed-loop infrastructure for materials discovery, manufacturing, and battery testing that utilizes a common data infrastructure and autonomous workflows to bridge big data from all domains of the battery value chain, can pave the way for a transformative reduction in the required time to discovery. ![]() With an exponentially growing demand for rechargeable batteries, the development of new ultra-performant, fully scalable, and sustainable battery technologies and materials must be accelerated.
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