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].

上海交通大学人工智能与微结构实验室李金金教授和黄富强教授团队研发出全新 AI 材料设计模型 CGformer，成功突破传统晶体图神经网络局限。 人工智能正深刻重塑材料科学研发范式，在加速新材料发现与性能优化中展现出突破性价值。通过高通量计算与机器学习的深度融合，传统「试错法」存 在的实验周期长、资源消耗大等痛点被有效破解，材料探索进入到「计算驱动-实验验证」的高效迭代阶段。然而随着人类技术和生活方式的革新，新能 源、航空航天等领域对新材料的性能需求日益严苛，传统机器学习方法的局限性逐渐凸显，尤其是在高熵材料研发领域。 所谓「高熵」材料，是一类由多主元元素混合制备的新型材料。高熵材料通过多主元协同作用显著提升原子排列的构型熵（即无序性），从而赋予其相较 传统材料更优异的力学、耐高温、耐腐蚀等综合性能，在能源存储、航空航天、极端环境装备等领域具有重要的应用潜力。 此前方法如 Crystal graph convolutional neural networks（CGCNN）、Atomistic line graph neural network（ALIGNN） 等人工智能模型均存在架构上的缺 陷：受限于局部信息交 ...

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]