2025年中国AIforScience行业概览：创新驱动：AI如何助力科学创新的无限可能

Investment Rating - The report does not explicitly provide an investment rating for the AI for Science industry. Core Insights - The AI for Science industry is defined as the application of artificial intelligence technologies to accelerate scientific research and discovery, leveraging data-driven methods to uncover patterns in vast datasets [9][10]. - The evolution of scientific paradigms has transitioned from empirical observation to AI-assisted research, marking a significant advancement in scientific methodologies [23][25]. - The development of AI for Science is ongoing, with expectations for deeper integration of AI technologies into scientific research, enhancing the discovery of new knowledge [27][29]. Summary by Sections Industry Overview - AI for Science combines data-driven and model-driven approaches to enhance scientific research efficiency and accuracy, enabling exploration of complex systems without extensive theoretical backgrounds [9][16]. - The paradigm shift in scientific research reflects a gradual evolution, with each stage building on previous technological advancements [23][25]. Technical Analysis - Core technologies in AI for Science include high-performance computing infrastructure, data management systems, scientific computing software, pre-trained models, and high-throughput experimentation, all of which facilitate accelerated scientific research [31][34]. - High-performance computing is crucial for processing large datasets and training complex machine learning models, significantly improving research efficiency [36][37]. - High-throughput experimentation integrates automation to rapidly execute complex experimental designs, generating substantial data for machine learning model training [41][43]. 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 research and technological innovation [45][49]. - In the life sciences, AI accelerates drug discovery, optimizes genomic research, and enhances personalized medicine through data analysis [50][52]. - The application of AI in earth sciences improves data analysis and predictive modeling, aiding in understanding complex earth system issues [58]. - AI for Science in materials chemistry enhances the understanding and performance of materials through accurate data analysis and predictive modeling [61].