Nowadays, lithium-ion batteries (LIBs) are widely used in portable electronics, electric vehicles, and grid-scale energy storage systems. However, the commercial graphite anode has almost reached its theoretical capacity limit of 372 mAh g⁻¹, and conventional cathode materials still face problems such as low specific capacity and unstable electrode-electrolyte interfaces, which limit the cycle life and safety of the batteries. Nanotechnology can provide useful ways to deal with these problems, because it shortens the diffusion path of lithium ions, buffers the volume strain during charge and discharge, and stabilizes the interfacial chemistry between electrode and electrolyte. This paper reviews the recent development of nanotechnology in both cathode and anode materials. For cathode materials, elemental doping can stabilize the crystal lattice and improve ion transport, surface coatings can reduce the dissolution of transition metals and side reactions with the electrolyte, and morphology control can further shorten diffusion paths and improve rate capability. For anode materials, carbon coating, porous structures, and one-dimensional nanowires can relieve volume expansion and make the solid-electrolyte interphase more stable. This review also discusses several key challenges, such as nanoparticle agglomeration, side reactions at the interface, and manufacturing cost, and gives some prospects for future multi-scale synergistic design.

Perovskite oxides with the ABO₃ structure are core candidates for dielectric energy-storage capacitors, whose recoverable energy density is governed by the dielectric constant, the breakdown field strength, and the nonlinear polarization response. This review surveys recent machine-learning studies on predicting these properties of oxide perovskites, covering three representative quantities—the dielectric constant, the breakdown field strength, and the ferroelectric Curie temperature—together with direct performance indicators such as recoverable energy density and efficiency. The works are compared along the evolution of methods, from manual descriptors and ensemble learning to graph neural networks, small-sample strategies, and generative inverse design, with dataset size, verification level, and accuracy documented. Three insights emerge: physical descriptor models remain competitive and interpretable under small-sample conditions; equivariant graph neural networks offer advantages for tensorial and finite-temperature tasks at higher data and computational cost; and generative inverse design is emerging, with preliminary success at the ferroelectric mesoscopic scale, but remains limited by scarce multimodal data and incomplete experimental-validation loops, which constitute the main challenges ahead.