新材料作为战略性、基础性产业，是加快发展新质生产力、扎实推进高质量发展的重要产业方向。 材料基因工程(materials genome engineering, MGE)深度融合了计算模拟 、高通量实验与数据科学 ，显著 提 升 了 新 材 料 的 研 发 效率，其材料数据库与跨尺度模型正成为人工智能在材料设计、性能预测等环节深度应用构建的技术基座。 随着人工智能技术的飞速发展，MGE 与智能科学的结合正迎来前所未有的机遇与挑战。 本文综述了在 AI+ 时代，材料人工智能的出现背景与历史，以及材料数据基础设施和所使用到的 AI 技术，整理了机器学习和自然语言处理等技术在材料逆向设计与筛选、物性预测与表征分析和性能优化等方面的应用，介绍了自主实验室系统的范式创新。 最后，展望并提出了 AI 在材料科学领域面临的挑战，以及未来的完善方向和建议。

Asa strategic and fundamental industry, new materials represent a crucial industrial direction for accelerating the development of new-quality products and steadily promoting high-quality development. Materials genome engineering (MGE) deeply integrates computational simulation, high-throughput experiments, and data science, significantly enhancing the R&D efficiency of new materials. Its material databases and cross-scale models are becoming the technological foundation for the in-depth application of artificial intelligence in material design, performance prediction, and other aspects. With the rapid development of artificial intelligence technology, the combination of MGE and intelligent science is facing unprecedented opportunities and challenges. This paper reviews the background and history of the emergence of artificial intelligence in materials in the AI+ era, as well as its material data infrastructure and the AI technologies used. It summarizes the applications of technologies such as machine learning and natural language processing in material reverse design and screening, physical property prediction and characterization analysis, and performance optimization. It also introduces the paradigm innovation of autonomous laboratory systems. Finally, this paper looks ahead to the potential challenges that AI may face in the field of materials science and proposes directions and suggestions for future improvement.