Quantum-artificial intelligence framework for suppressing dendrites and enhancing interface stability in lithium-metal solid-state batteries

With the increasing call for sustainable energy storage, solid-state batteries (SSBs) stand out as promising for dealing with the safety, energy density, and lifespan performance bottlenecks associated with commercial lithium-ion systems. Nonetheless, factors such as dendrite formation and unstable electrode–electrolyte interfaces still hamper their commercial adoption in grid-scale renewable applications. In this work, we present a quantum-informed artificial intelligence framework that combines quantum chemistry-based interfacial descriptors and machine learning algorithms to predict, suppress, and optimize dendritic propagation and interfacial breakdown in lithium-metal SSBs. The framework benefits from density functional theory-based quantum simulations to guide the training of graph-based artificial intelligence predictors, thus offering material and interface design in a realistic operational environment. Our work shows enhanced thermal stability, ionic conductivity, and interface coherence and provides a strategic step toward the practical applications of deployable SSBs for smart grids. The model is consistent with the Sustainable Development Goal objectives for affordable energy, climate action and innovation, and it represents a road for scalable, sustainable, and intelligent energy storage.