AI-driven sustainable supercapacitors for advanced renewable energy storage applications

The accelerating transition to renewable energy has intensified the demand for sustainable, high-performance energy storage systems that address intermittency while minimizing environmental impact. Supercapacitors have gained attention due to their high-power density, rapid charge-discharge capability, and long cycle life, making them valuable complements or alternatives to batteries in applications from grid storage to portable and wearable devices. Advances in materials chemistry have driven significant improvements, with innovations in electrode design, electrolyte formulation, and device architecture enhancing energy density and broadening functionality. Sustainability considerations are now central, with emphasis on biomass-derived carbons, eco-friendly electrolytes, recyclable components, and life cycle assessment (LCA) approaches to align progress with circular economy goals. Artificial intelligence (AI) and machine learning (ML) are also transforming supercapacitor research by accelerating material screening, optimizing nanostructures, predicting electrochemical performance, and guiding autonomous synthesis. These tools reduce costs, shorten development timelines, and improve lifetime prediction through degradation modelling. Nonetheless, challenges persist in closing the energy density gap with batteries, scaling sustainable materials, and standardizing AI integration. This review highlights breakthroughs in materials, sustainability, and AI/ML-enabled approaches shaping next-generation supercapacitor technologies for renewable energy futures.