Lithium-ion batteries (LIBs) have become one of the best solutions to the energy storage issue in modern society. However, the battery materials and device development are both complex, and involve multivariable problems. Traditional trial-and-error approach, which relies on researchers to conduct experiments, has encountered bottlenecks in the improvement of the battery performance. Artificial intelligence (AI) is the most potential technology to deal with this issue due to its powerful high-speed and capabilities of processing massive data. In particular, the capability of machine learning (ML) algorithms in assessing multidimensional data variables and discovering patterns in the sets are expected to assist researchers in discovering patterns and elucidating the mechanisms of material synthesis and device fabrication. This review summarizes various challenges encountered in traditional research methods of LIBs and introduces the applications of AI in battery material research, battery device design and manufacturing, material and device characterizations, and battery cycle life and safety assessment in detail. Most importantly, we present the challenges faced by AI and ML in battery research, and discuss the shortcomings and prospects of their applications. We believe that a closer collaboration among experimentalists, modeling specialists, and AI experts in the future will greatly facilitate AI and ML methods for solving battery and materials problems that are difficult to be solved by traditional methods.

The growth of Li dendrites and the instability of the solid electrolyte interphase (SEI) layer during plating/stripping has hindered the practical application of high-energy-density batteries based on a lithium metal anode. Building a stable interfacial layer is effective in preventing lithium corrosion by the electrolyte and controlling the deposition of lithium metal. Here, we present a robust polydopamine-Cu ion (PDA-Cu²⁺) coating layer formed by the aggregation of nanoparticles and Cu ions, which can be obtained by a subtle immersion strategy. We demonstrate that the PDA-Cu²⁺ protective layer, with a unique structure comprising nanoparticles, can regulate and guide Li metal deposition, and together with Cu ions, forms a lubricating surface to facilitate uniform Li ion diffusion and induce stable SEI layer formation. Li anodes with this PDA-Cu²⁺ layer modification ultimately achieve higher Coulombic efficiencies, which are consistently stable for over 650 cycles at 0.5 mA·cm^-2 without Li dendrites. The introduced PDA-Cu²⁺ coating can adhere to any material of any shape; additionally, the operation can be realized on a large scale because of its simplicity. These merits provide a promising approach for developing stable and safe lithium metal batteries.