Investment Rating - The report does not explicitly provide an investment rating for the AI for Science industry. Core Insights - The AI for Science industry leverages artificial intelligence to accelerate scientific research and discovery, utilizing data-driven and model-driven approaches to enhance efficiency and accuracy in scientific endeavors [9][10][12]. Summary by Sections Industry Overview - AI for Science is defined as the use of AI technologies to expedite scientific research and discovery, employing big data and machine learning to uncover hidden patterns [9][10]. - The evolution of scientific paradigms has transitioned from direct observation to AI-assisted research, marking significant advancements in scientific methodologies [24][26]. - The current stage of AI for Science is characterized by a deep integration of AI technologies into scientific research, enhancing predictive capabilities and fostering innovation [28][30]. Technical Analysis - Core technologies in AI for Science include high-performance computing, data management infrastructure, scientific computing software, pre-trained large models, and high-throughput experiments, all of which facilitate accelerated scientific research [32]. - High-performance computing is crucial for processing large datasets and training complex machine learning models, significantly improving research efficiency [35][38]. - High-throughput experimentation enables rapid execution of complex experimental designs, generating vast amounts of data for machine learning model training [42][45]. Industry Development Practices - AI for Science is a cross-disciplinary field that applies AI technologies to traditional scientific domains such as physics, chemistry, biology, and medicine, showcasing its potential to drive scientific research and technological innovation [46][51]. - In the life sciences, AI is transforming drug development, optimizing genomic research, and enhancing personalized medicine through data analysis and predictive modeling [53][56]. - The application of AI in earth sciences improves data analysis and predictive modeling, aiding in climate change research and geological disaster prediction [62]. - In materials chemistry, AI enhances data analysis and predictive modeling, helping scientists understand and address complex material systems [65].