Core Viewpoint - The article discusses the launch of SES AI's battery-specific AI4S solution, "Molecular Universe 1.0" (MU-1.0), which aims to revolutionize battery material research by significantly reducing the time required for performance validation from years to minutes through advanced predictive capabilities [3][6][8]. Group 1: Battery Performance Requirements - The electricization wave is creating diverse performance demands across various end products, from AI glasses with a 4-hour battery life to humanoid robots and electric engineering machinery requiring up to 6000 cycles [2]. - Battery performance has become a competitive focus across emerging industries, including consumer electronics, robotics, and heavy machinery [3]. Group 2: MU-1.0 Features and Innovations - MU-1.0 is regarded as SES AI's most comprehensive solution to date, with a key update in the Predict module that establishes a correlation between molecular materials and battery performance [4]. - The solution allows users to replace unknown molecules in a selected battery chemistry and observe their impact on cycle life, enabling predictions even for entirely unknown chemical systems based on early cycle life data [5]. - The development process for performance validation is expected to shift from a multi-year cycle of design, testing, and verification to a computational simulation that takes only minutes, greatly enhancing research efficiency [6]. Group 3: Additional Modules and Applications - MU-1.0 integrates several modules, including intelligent Q&A, a comprehensive battery molecular database, molecular search tools, and precise prediction capabilities for electrolyte formulations [11][12]. - The application of "Molecular Universe" spans critical areas such as improving low-temperature cycle life for lithium iron phosphate batteries, enhancing safety and energy density for high-silicon anode lithium-ion batteries, and optimizing voltage stability for lithium cobalt oxide batteries [11][12]. Group 4: Future Developments - More technical details and demonstrations of MU-1.0 will be revealed during its official launch on October 20, 2025 [9].