美国启动能源版“曼哈顿计划”，举国搭建AI4S平台

Core Viewpoint - The article discusses the launch of the Genesis Mission by the U.S. government, which aims to establish a national-level discovery platform integrating AI, quantum computing, and advanced experimental facilities to enhance AI for Science (AI4S) as a national strategic priority [2]. Group 1: Platform Objectives - The platform aims to break data silos and create a closed-loop system consisting of "data, computing power, and experiments" [3]. - The data layer will aggregate decades of classified and proprietary research data from the federal government to build high-quality scientific models, addressing the challenge of AI lacking high-quality training data [3]. - The computing power layer will involve partnerships with tech giants like NVIDIA, AMD, Microsoft, Google, and AWS to provide GPUs, cloud platforms, and engineering teams [4]. - The physical layer will deploy robotic chemists and automated synthesis facilities to create a "wet-dry closed loop," enabling AI-generated formulas to be automatically synthesized and validated [5]. Group 2: Implementation Timeline - The executive order sets an aggressive timeline: within 60 days, the Department of Energy must submit a list of at least 20 "national challenges" covering advanced nuclear energy, grid modernization, critical materials, semiconductors, and high-end manufacturing [6]. - Within 90 days, a comprehensive inventory of federal computing and data resources must be completed [6]. - A complete implementation plan and budget pathway must be presented within 9 months, defining platform architecture, data access rules, and methods for engaging industries and universities [7]. Group 3: Focus Areas - The initiative highlights several key areas for energy and materials: 1. Accelerating fusion and advanced nuclear energy research using AI and high-performance computing, including reactor design and materials development [8]. 2. Optimizing grid operations and planning with AI under the "grid modernization" framework to enhance supply efficiency and stability amid rising electricity demand and increasing renewable energy share [8]. 3. Designing alternative solutions for critical materials and optimizing resource utilization and recycling processes with AI to reduce dependence on foreign supply chains [8]. Group 4: Challenges and Concerns - The plan addresses two major pain points in AI4S: breaking data silos and overcoming synthesis bottlenecks, as the lack of high-quality, standardized experimental data and slow validation processes are significant obstacles [9]. - There is a concern that the public research infrastructure may evolve into a data and computing power flywheel dominated by a few tech giants [11]. - The quality of data and classification levels will determine whether this platform can genuinely transform the research paradigm [11].