人工智能赋能的超级电容器材料研发与器件管理OA

Supercapacitors(SCs)play a crucial role in modern energy storage technologies,offering advantages,including high power density,fast charge/discharge rates,and a long cycle life.However,their relatively low energy density limits applications in fields requiring prolonged endurance,such as new energy vehicles and smart grids.Recently,the rapid development of artificial intelligence(AI)has opened new avenues for overcoming this performance limitation.Based on a dual-stage framework of"materials development-device management,"this review systematically surveys recent AI advancements across the full lifecycle of SCs.In the materials development phase,we assess the current landscape of open computational materials databases and specialized electrochemical databases for supercapacitors.We also discuss the evolutionary trajectory of feature engineering for descriptors.The evolution of deep learning prediction models is comprehensively surveyed,highlighting early graph neural networks,universal neural networks,and large-scale foundation models.We examine the paradigm shift from manual feature engineering to end-to-end representation learning and its significant enhancement of high-throughput virtual screening efficiency.We also introduce Bayesian optimization and active learning-driven synthesis optimization strategies,as well as the closed-loop paradigm of"prediction-synthesis-validation-feedback."In addition,we describe generative inverse design methods based on diffusion models and autoregressive models,analyzing their potential for the direct generation of candidate material structures under target property constraints.In the device operational stage,we draw insights from established methodologies in the lithium-ion battery field to systematically survey the evolution of state-of-health assessment and remaining useful life prediction.This encompasses physics-based models,traditional machine learning,deep learning,state-space models,generative pre-training,and physics-informed neural networks,as well as applications of transfer learning and federated learning in data-scarce and privacy-sensitive contexts.Looking ahead,we identify several key pathways for advancing AI from a supplementary analytical tool to a central research infrastructure across the entire value chain.These include constructing unified supercapacitor databases based on established principles,developing global descriptors tailored to pore network topology,and achieving deep integration of autonomous experimental platforms with closed-loop feedback systems.