人工智能驱动的材料科学：演进、框架、困境与破局

Artificial intelligence-driven materials science: Evolution, framework, dilemmas, and breakthroughs

        周杨理理^1,2(中国科学院东莞材料科学与技术研究所 东莞 523830;松山湖材料实验室 东莞 523830)
        汪卫华¹(中国科学院东莞材料科学与技术研究所 东莞 523830)
        赵紫威^1,2*(中国科学院东莞材料科学与技术研究所 东莞 523830;松山湖材料实验室 东莞 523830)

         人工智能驱动的材料科学;人工智能;材料科学

        AIMS;artificial intelligence;materials science

        人工智能驱动的材料科学（artificial intelligence-driven materials science,AIMS）带来了材料研发范式革命，有望从根本上突破材料传统研发周期与效率瓶颈。从历史看，材料科学研究范式从经验试错、理论建模、计算模拟演进至数据驱动的新阶段，是由认知工具和方法革新带来的。当下，人工智能（AI）作为颠覆性的认知工具，更是从根本上重构了材料科学核心要素及其互动逻辑：科研过程实现智能迭代与全流程闭环；科研人员能力重塑与团队有组织化；科研对象深度广度拓展与需求精准锁定，从而形成了材料研发新模式和新范式，这正是新质生产力在材料科学领域孕育发展的体现。然而，当前我国在AIMS领域仍面临多尺度建模、多模态数据融合、模型泛化等瓶颈，需通过夯实数据底座、攻关核心技术、完善生态保障等路径突破。文章综述了材料科学研究范式的演进历程与现状，构建了AI驱动材料科学要素变革的理论框架，分析了AIMS面临的困难瓶颈，提出了推进我国AIMS发展的战略路径和策略，以期为加速AIMS范式体系形成、培育材料新质生产力提供理论与实践参考。

        Artificial intelligence-driven materials science(AIMS) represents a revolutionary and disruptive paradigm in materials research, promising to fundamentally break through the traditional bottlenecks of research cycles and efficiency. Historically, the evolution of materials science research paradigms from empirical trial and error, theoretical modeling, and computational simulation to the new data-driven stage has been driven by innovations in cognitive tools and methods. Currently, artificial intelligence, as a disruptive cognitive tool, is fundamentally reconstructing the core elements and interaction logic of materials science: the research process achieves intelligent iteration and full-process closed-loop; the capabilities of researchers are reshaped and teams are organized; and the depth and breadth of research objects are expanded and precise demands are locked in, thus forming a new model and paradigm for materials research. This is precisely the manifestation of the emergence and development of new quality productivity in the field of materials science. Nevertheless, China still faces bottlenecks such as multi-scale modeling, multi-modal data fusion, and model generalization in the AIMS field. These need to be overcome through solidifying the data foundation, tackling core technologies, and improving ecological support, in order to achieve a fundamental transformation of the materials science research paradigm. This study reviews the evolution and current status of research paradigms in materials science, constructs a theoretical framework for the transformation of elements in AIMS, analyzes the difficulties and bottlenecks faced by AIMS, and proposes strategic paths and strategies for promoting the development of AIMS in China, with the aim of providing theoretical and practical references for accelerating the formation of the AIMS paradigm system and fostering new material productivity.