Core Insights - The global technological competition is advancing towards foundational research and interdisciplinary fields, with AI for Science (AI4S) becoming a core engine for achieving high-level technological self-reliance and reconstructing scientific research paradigms [1] Group 1: Challenges in AI4S Development - The field of condensed matter science faces challenges in developing AI4S due to its reliance on researcher experience and intuition, leading to high trial-and-error costs and long R&D cycles [2] - The transition from a "human-intensive" trial-and-error approach to a "data and intelligence-intensive" rational design and prediction is expected to significantly enhance R&D efficiency and shorten the cycle from basic research to industrial application [2] Group 2: Data Quality and Governance - High-quality scientific data is the foundation of AI4S, with researchers working to build data aggregation and integration platforms to optimize AI model performance [3] - Current issues in condensed matter science data include resource scarcity, severe data isolation, insufficient data volume, inconsistent standards, and a lack of effective data aggregation mechanisms [3] - Data governance technology is a driving force for AI4S, with research teams developing efficient data processing algorithms and tools to enhance data governance and support scientific breakthroughs [4] Group 3: Innovation Ecosystem - A data innovation ecosystem is essential for the sustainable development of AI4S, with efforts to create research platforms and communities that support data sharing and collaboration [5] - Existing practices include standardized evaluation systems for models related to crystal material topology and XRD intelligent structure analysis, which support large-scale, collaborative data-driven technological innovation [5] Group 4: National Strategies and Initiatives - The digital transformation of condensed matter science has become a national strategy for major economies, with initiatives like the U.S. "Genesis Project" and the U.K. "National AI Strategy" focusing on the deep integration of condensed matter science and AI [6] - China is actively exploring this field, with the Chinese Academy of Sciences' Condensed Matter Science Data Center making systematic progress in integrating experimental, theoretical, and computational data [6] Group 5: Future Directions for Development - To further promote AI4S in condensed matter science, efforts should focus on building standardized, high-quality national data foundations and enhancing data collection, governance, application, and sharing services [7] - Creating an open, collaborative, and sustainable data ecosystem is crucial, along with fostering deep integration between industry, academia, and research [7] - Breakthroughs in data technology will enhance China's independent innovation capabilities and international competitiveness in condensed matter science [7]