第二个方面是加强推广应用，聚焦新材料进入市场初期面临的推广应用瓶颈，深入组织实施首批次新材 料保险补偿政策，扩大首批次新材料支持数量和年限范围，打消用户采购顾虑，并鼓励各地因地制宜完 善首批次政策，加快首批次材料推广。目前，已累计推动价值超550亿元新材料产品进入市场，助力神 舟二十一号载人飞船等飞天逐梦，支撑C919大飞机翱翔蓝天、智能动车组驰骋神州。 第三个方面是加强能力建设，布局形成涵盖材料研发、中试熟化、材料生产、测试验证、材料应用等全 过程的5类新材料重点平台体系，加快新材料与终端产品同步测试验证和迭代升级，发布500余项新材料 标准，引领先进基础材料、关键战略材料、前沿新材料等优化升级。 "下一步，工信部将立足满足重点应用领域现实需求，以材料创新引领产业发展为目标，以先进基础材 料、关键战略材料、前沿新材料、'人工智能+材料'为发展方向，全链条推动先进材料上下游协同创 新，强化政策统筹、资金扶持、人才供给和要素保障，打造促进新材料研发应用的良好生态，全面提升 新材料产业创新能力和发展效能。"陶青强调。 中化新网讯1月21日，国务院新闻办公室举行新闻发布会。工信部新闻发言人、运行监测协调局局长陶 青 ...

（来源：经济参考报） 工业和信息化部运行监测协调局局长陶青日前说，工业和信息化部会同相关部门、各地方，大力推动新 材料科技创新和产业创新深度融合，新材料发展水平和供给保障能力明显提升。其中，已累计推动价值 超550亿元新材料产品进入市场。 陶青表示，下一步，工业和信息化部将立足满足重点应用领域现实需求，以先进基础材料、关键战略材 料、前沿新材料、"人工智能+材料"为发展方向，打造促进新材料研发应用的良好生态，全面提升新材 料产业创新能力和发展效能。 此外，在加强能力建设方面，布局形成了涵盖材料研发、中试熟化、材料生产、测试验证、材料应用等 全过程的5类新材料重点平台体系，加快新材料与终端产品同步测试验证和迭代升级，累计提供近3000 批次新材料应用验证服务、150余万次测试评价服务。 陶青在当天举行的国新办新闻发布会上说，有关方面加强重点突破，组织实施好国家科技重大专项、产 业基础再造工程等，一批关键材料实现突破；加强推广应用，聚焦新材料进入市场初期面临的推广应用 瓶颈，深入组织实施首批次新材料保险补偿政策，扩大首批次新材料支持数量和年限范围，并鼓励各地 因地制宜完善首批次政策，加快首批次材料推广。 ...

Core Insights - The Ministry of Industry and Information Technology (MIIT) has significantly enhanced the development level and supply assurance capability of new materials, with over 55 billion yuan worth of new material products introduced to the market [1][2] Group 1: Policy and Initiatives - MIIT, in collaboration with relevant departments and local governments, is promoting the deep integration of new material technology innovation and industrial innovation [1] - Key breakthroughs have been achieved in critical materials through the implementation of national science and technology major projects and industrial foundation reconstruction projects [1] - The introduction of insurance compensation policies for first-time new materials aims to address market entry challenges and expand support for these materials [1] Group 2: Capacity Building - A comprehensive system of five key platforms covering the entire process of material research and development, pilot testing, production, testing, and application has been established [1] - Nearly 3,000 batches of new material application verification services and over 1.5 million testing and evaluation services have been provided [1] Group 3: Future Directions - MIIT plans to focus on advanced basic materials, key strategic materials, cutting-edge new materials, and the integration of artificial intelligence with materials to enhance innovation capabilities and development efficiency in the new materials industry [2]

Metal Market - Domestic base metals experienced a general increase, with Shanghai lead and zinc both declining by 0.67% and 0.2% respectively. Shanghai tin led the gains with a rise of 5.79%, while other metals saw increases of less than 1% [1] - In the external market, base metals also showed positive performance, with London tin rising by 5.68% and nickel increasing by 1.75%. Other metals had gains of less than 1% [1] - Precious metals saw significant movements, with COMEX gold rising by 2.13% to a peak of $4891.1 per ounce, nearing the $4900 mark, setting a new historical high. COMEX silver fell by 0.18%, while domestic gold rose by 3.69% to a peak of 1101.92 yuan per gram, also setting a new historical high. Domestic silver decreased by 0.11% [1] Macro Environment - The Ministry of Industry and Information Technology announced plans to promote collaborative innovation in advanced materials across the entire supply chain, aiming to enhance the innovation capacity and development efficiency of the new materials industry [3] - The number of 5G users in China has exceeded 1.2 billion, with 483.8 million 5G base stations built, covering all towns and 95% of administrative villages. The country is also advancing in 6G technology, having completed the first phase of technical trials and starting the second phase [4] - The People's Bank of China conducted a net injection of 122.7 billion yuan through reverse repos, maintaining the operation rate at 1.40% [5] Oil Market - Both domestic and international oil prices fell, with U.S. oil down by 0.93% and Brent oil down by 0.92%. The U.S. President indicated that Venezuelan oil could help lower U.S. oil prices, while EIA forecasts a moderate recovery in crack spreads by 2026, with increasing refined oil inventories exerting short-term downward pressure on crude oil prices [8]

第一个方面是加强重点突破。组织实施好国家科技重大专项、产业基础再造工程等，强化企业创新主体 地位，围绕下游重点应用领域建立上下游合作机制，开展体系化协同创新。"十四五"以来，突破了一批 关键材料，如高性能碳纤维复合材料全球首次应用于商业化运营地铁列车车体等主承载结构，实现整车 减重11%，每年可以减少二氧化碳排放约130吨，推动城市轨道交通向轻量化、绿色化升级。 第二个方面是加强推广应用。聚焦新材料进入市场初期面临的推广应用瓶颈，深入组织实施首批次新材 料保险补偿政策，扩大首批次新材料支持数量和年限范围，打消用户采购顾虑，并鼓励各地因地制宜完 善首批次政策，加快首批次材料推广。截至目前，已累计推动价值超550亿元新材料产品进入市场，助 力神舟二十一号载人飞船等飞天逐梦，支撑C919大飞机翱翔蓝天、智能动车组驰骋神州。 第三个方面是加强能力建设。布局形成了涵盖材料研发、中试熟化、材料生产、测试验证、材料应用等 全过程的5类新材料重点平台体系，加快新材料与终端产品同步测试验证和迭代升级，累计提供近3000 批次新材料应用验证服务、150余万次测试评价服务，服务企业23万家。发布500余项新材料标准，引领 先进基础 ...

人民财讯1月21日电，1月21日，国新办举行新闻发布会介绍2025年工业和信息化发展成效。工业和信息 化部运行监测协调局局长陶青表示，"十四五"以来，一批关键材料实现突破，高性能碳纤维复合材料全 球首次应用于商业化运营的地铁列车车体等主承载结构，实现整车减重11%。下一步，工信部将立足满 足重点应用领域的现实需求，以材料创新引领产业发展为目标，以先进基础材料、关键战略材料、前沿 新材料，人工智能+材料为发展方向，全链条推动先进材料上下游协同创新，强化政策统筹、资金扶 持、人才供给和要素保障，打造促进新材料研发应用的良好生态，全面提升新材料产业创新能力和发展 效能。 ...

Core Points - The 9th International Forum on Materials Gene Engineering will be held from November 19-23, 2025, in Xi'an, Shaanxi Province, China, aiming to promote the development of foundational theories, cutting-edge technologies, and key equipment in the field of materials gene engineering [1][2]. Group 1: Forum Overview - The forum has successfully held 8 sessions since 2017, attracting over 310 academicians and more than 8,000 domestic representatives, significantly impacting the development and application of disruptive technologies in materials science [1]. - The event is co-hosted by the National New Materials Big Data Innovation Alliance and the Chinese Materials Research Society, with several universities and research institutes as co-organizers [2]. Group 2: Themes and Topics - Key themes include efficient material computation and intelligent design, transformative experimental technologies, AI applications in materials science, big data in materials, and the intelligent development and application of the materials industry [2][12][15][18][20]. Group 3: Event Schedule - Important dates include online registration and poster submission by November 9, 2025, on-site registration on November 19, and various sessions and meetings scheduled from November 20 to 23 [2][5]. Group 4: Organizational Structure - The organizing committee includes prominent figures from various institutions, with a focus on advancing materials science through collaboration and innovation [3][4]. Group 5: Registration Information - Registration fees are set at RMB 2,800 (approximately USD 400) for formal representatives and RMB 1,800 (approximately USD 260) for student representatives, with accommodation and meals arranged during the forum [5]. Group 6: Academic Reports - The forum will feature a range of academic reports from distinguished speakers, including members of the Chinese Academy of Engineering and international experts, covering various aspects of materials science and engineering [8][9][10]. Group 7: Supporting Institutions - Numerous supporting institutions, including universities and research centers, are involved in the forum, highlighting the collaborative effort in advancing materials gene engineering [3][4]. Group 8: Future Directions - The forum aims to accelerate the integration of artificial intelligence in materials research and development, fostering innovation and collaboration across the industry [1][2][20].

Core Insights - The article discusses the development of a new AI material design model called CGformer by professors Li Jinjing and Huang Fuqiang from Shanghai Jiao Tong University, which successfully overcomes the limitations of traditional crystal graph neural networks [1][2] - The integration of high-throughput computing and machine learning is transforming material science research, accelerating the discovery and optimization of new materials [1][2] - CGformer addresses the challenges in developing high-entropy materials, which have complex microstructures and require advanced predictive capabilities [2][4] Group 1: Model Development and Innovation - CGformer combines the global attention mechanism of Graphormer with the crystal graph representation of CGCNN, allowing it to capture long-range atomic interactions and global information [2][6] - The model provides comprehensive structural information, aiding in the accurate prediction of ionic migration behaviors, particularly for high-entropy and complex crystal materials [3][4] - The architecture of CGformer enhances the model's ability to represent complex crystal structures and improves prediction accuracy compared to traditional models [6][9] Group 2: Performance and Validation - In research on high-entropy sodium solid electrolytes (HE-NSEs), CGformer achieved a 25% reduction in mean absolute error compared to CGCNN, demonstrating its practical utility [4][10] - The model successfully filtered 18 out of 148,995 potential high-entropy structures, synthesizing and validating 6 HE-NSEs with a room temperature sodium ion conductivity of up to 0.256 mS/cm [4][13] - CGformer exhibited superior stability and prediction accuracy during pre-training and fine-tuning phases, with a final mean absolute error of 0.0361 after fine-tuning [10][12] Group 3: Application and Future Potential - The research highlights the significant potential of AI in material science, particularly in the development of high-entropy materials, which are crucial for applications in energy storage and aerospace [1][16] - The integration of AI technologies in material research is becoming a mainstream approach, showcasing the strong development potential and application value of interdisciplinary research [16][19] - CGformer represents a significant advancement in the field, addressing key challenges in high-entropy material development and paving the way for future innovations [16][17]

Core Insights - The research team at Kunming University of Science and Technology has made significant breakthroughs in the intersection of "Artificial Intelligence + Materials" by proposing a "Continuous Transfer" machine learning framework, addressing the technical bottleneck of multi-performance prediction of materials with small datasets [1][2] - The framework allows for the efficient development of new functional materials, demonstrating the universality of transfer learning in optimizing multiple material properties [2] Group 1: Research Achievements - The team successfully constructed a "Continuous Transfer" learning strategy that first trains a high-precision base model using extensive formation energy data, followed by sequential predictions of key material properties such as stability, bandgap, and bulk modulus [1] - In a shear modulus prediction task with only 51 data points, the team utilized a bulk modulus model as a "stepping stone" for secondary transfer, significantly enhancing prediction reliability in small datasets [1] Group 2: Material Discovery - Using the framework, the research team rapidly screened over 18,000 candidate materials, identifying 54 inorganic double perovskite coating materials with high stability and excellent ductility [2] - Among these, cesium copper hexafluoroiridate exhibited outstanding performance, with a bandgap suitable for photovoltaic applications and a high ductility indicated by the ratio of shear modulus to bulk modulus [2] Group 3: Implications for the Industry - This research not only provides a candidate material library for fields such as perovskite solar cells and photocatalysis but also offers a scalable computational tool to address the challenges of data scarcity in material development [2] - The advancements in material informatics signify a crucial step in solving the "few data, many tasks" dilemma in material research, providing an efficient computational paradigm for multi-performance optimization [2]