来源：科技日报 俄罗斯 量产航空涂装材料 制备新型石墨烯薄膜 科技日报驻俄罗斯记者 张浩 2025年，俄罗斯新材料研发呈现出"军工优势向民用转化、极端环境材料突破"的鲜明特征。 面对航空工业与北极开发的战略需求，俄罗斯在材料工程化应用上取得实质性突破。全俄航空材料研究 院开发的新一代氟聚氨酯瓷漆实现量产，其自重较同类产品减轻35%，且涂装周期缩短一半以上，显著 提升了国产航空装备的维护效率。库尔恰托夫研究所展示了专为极地科考设计的耐寒钢及超低温韧性材 料，确保装备在零下60℃的极端环境下仍能保持优异的机械性能。 在催化剂定向合成方向，俄罗斯科学院库尔恰托夫研究所研发出基于合成硅铝酸盐的新型催化剂，实现 了木材废料向高附加值医药及香料化合物的高效转化。同时，针对能源存储痛点，科研团队通过异相溶 胶—凝胶法制备的高负载双金属镍基催化剂，有效提升了液态有机储氢载体脱氢过程的选择性与稳定 性，为清洁能源链条提供了核心技术支撑。 此外，尖端微观调控技术与军民融合体系的完善，进一步拓宽了材料的应用边界。莫斯科钢铁与合金学 院利用高能重离子轰击技术，制备出嵌有金刚石纳米结构的石墨烯薄膜，在超硬涂层与精密电子器件领 域表现出 ...

Core Insights - The materials industry is at a critical historical juncture, requiring a transformation to leverage AI technology for overcoming development bottlenecks and advancing from a materials power to a materials stronghold [1][3]. Group 1: AI-Driven Material Innovation - AI is enhancing material innovation by enabling rapid iteration, atomic-level manufacturing, and breakthroughs in high-stability materials for extreme environments [3][4]. - Emerging industries such as new energy and robotics are creating new demands for high-end materials, including advanced polyolefins and biodegradable materials [3][4]. - Material innovation is pivotal in the intersection of AI and the new technological revolution, with China positioned to transition from "catching up" to "leading" in this field [3][4]. Group 2: Industrial Paradigm Shift - AI is driving a systemic reconstruction of industrial development paradigms, particularly in the materials sector, through three dimensions: technological innovation, production manufacturing, and organizational management [4][5]. - The shift from traditional experience-based R&D to AI-driven digital and intelligent processes significantly enhances efficiency and precision in material design and testing [4][5]. - AI facilitates real-time global optimization in manufacturing, transforming production models from discrete to continuous and proactive [5]. Group 3: New Research Paradigms in Material Science - The materials science research paradigm is undergoing a fourth transformation, evolving from experience-driven to data and AI-driven approaches [6][8]. - Global practices demonstrate the disruptive value of AI in material research, with significant advancements in predicting new materials and optimizing existing ones [6][8]. Group 4: Empowering New Material Production and Applications - AI is transitioning the new materials industry from single-segment optimization to collaborative lifecycle applications, addressing core pain points in production and management [8][9]. - The materials industry is expected to see three major trends: reduced R&D costs, intelligent manufacturing, and the emergence of a digital twin ecosystem [8][9]. Group 5: Systematic Implementation Pathways - To harness AI's strategic value, a systematic implementation pathway is necessary, including data governance, high-quality data set construction, and a national materials data space [10][11]. - Establishing a layered AI model system and enhancing AI literacy among industry professionals are crucial for supporting the intelligent transformation of the materials sector [10][11]. Group 6: Future Outlook - The materials industry in China is poised for a revolutionary era of innovation driven by AI, with significant reductions in R&D cycles and production costs, ultimately supporting the nation's goals of becoming a manufacturing and technological powerhouse [12].