EXTENDED ABSTRACT: CElectrochemical activity is the foundation for the design and performance optimization of electrochemical energy storage materials. Traditionally, this relies on empirical qualitative regulation or extensive "synthesischaracterization" experimental studies, which cannot meet the rapid design needs of electrodes with complex compositions and structures. Therefore, developing quantitative computational methods for electrochemical activity is crucial. This report focuses on recent advancements in quantitative computational methods for electrochemical activity using differential computation of characteristic variables. It further applies high-throughput computation, machine learning, and crystal structure prediction to design and screen high-performance electrochemical energy storage materials. Addressing the high overpotential issue in lithium-oxygen batteries, a screening rule for surface acidity catalysts is proposed, which has been extended to the hydrogen evolution reaction catalytic system in fuel cells, showing good consistency with experimental results. For organic electrode materials, a systematic study of the lithium-ion and sodium-ion intercalation reactions and charge transfer mechanisms reveals that sodium-ion electrode materials require non-layered structural characteristics to achieve the synergistic activation of functional groups and carbon ring structures. In contrast, the weak bond (hydrogen bonds, van der Waals forces, polarization effects) channel structure in lithium-ion organic electrode materials serves as the activation site. Through the charge transfer mechanism, Faradaic reaction and intercalation pseudocapacitance reaction mechanisms are uncovered, enabling the design of battery materials with high energy density and high power density. This report will also discuss data- and knowledge-driven materials science models and the construction of intelligent experimental facilities, focusing on application research related to high-entropy, lithium-rich cathode materials.