人工智能驱动的新材料研发：发展现状、全球格局及未来展望

Core Viewpoint - The article emphasizes the transformative potential of artificial intelligence (AI) in the field of new materials, highlighting its role in accelerating research and development processes, reshaping industry dynamics, and fostering new business models in the materials sector [1][4][25]. Group 1: AI and New Materials Development - The transition from traditional experimental methods to AI-driven approaches in new materials research has significantly increased the speed of development, with AI now serving as a core engine rather than just an auxiliary tool [4][8]. - The integration of AI with materials science has led to the emergence of new methodologies, such as the Materials Genome Initiative, which aims to systematically build material data resources and enhance collaboration across the globe [6][7]. - AI4MSE (AI for Materials Science and Engineering) relies on high-quality data resources, tailored machine learning algorithms, and applications that span the entire lifecycle of materials, from design to service [6][8]. Group 2: Global Competition in AI and New Materials - Major global powers are elevating AI4MSE to a strategic national priority, with the U.S. investing heavily in AI-driven materials research to maintain its leadership in the field [11][12]. - China is focusing on a systematic approach to build an AI and new materials innovation ecosystem, with significant government initiatives aimed at accelerating scientific discovery through AI [12][14]. - The European Union is promoting AI and new materials integration through policies that emphasize technological sovereignty and data sharing, aiming to enhance its competitive edge in advanced manufacturing [14][16]. Group 3: Commercialization and Industry Transformation - The commercialization of AI4MSE is rapidly accelerating, leading to the emergence of specialized startups that provide innovative materials design solutions directly to end-users [18][20]. - Traditional materials companies are also adapting by establishing digital R&D departments and integrating AI technologies to enhance product performance and reduce development cycles [22]. - A new "R&D as a Service" model is emerging, allowing companies without in-house AI capabilities to leverage AI-driven platforms for materials research, thus democratizing access to advanced materials development [23][24]. Group 4: Future Outlook and Challenges - The AI-driven revolution in new materials is expected to create numerous high-value service industries, while also driving significant upgrades in downstream sectors such as electronics and healthcare [25][27]. - Despite the promising outlook, challenges remain, including data quality issues, the interpretability of AI models, and the complexity of multi-scale modeling in materials science [27][28][29]. - The ongoing evolution of AI4MSE is set to reshape the competitive landscape of the materials industry, with a shift from product competition to competition based on research efficiency and innovation capabilities [24][29].