The age of cognitive computing and artificial intelligence (AI) is just dawning. Inspired by its successes and promises, several AI ecosystems are blossoming, many of them within the domain of materials science and engineering. These materials intelligence ecosystems are being shaped by several independent developments. Machine learning (ML) algorithms and extant materials data are utilized to create surrogate models of materials properties and performance predictions. Materials data repositories, which fuel such surrogate model development, are mushrooming. Automated data and knowledge capture from the literature (to populate data repositories) using natural language processing approaches is being explored. The design of materials that meet target property requirements and of synthesis steps to create target materials appear to be within reach, either by closed-loop active-learning strategies or by inverting the prediction pipeline using advanced generative algorithms. AI and ML concepts are also transforming the computational and physical laboratory infrastructural landscapes used to create materials data in the first place. Surrogate models that can outstrip physics-based simulations (on which they are trained) by several orders of magnitude in speed while preserving accuracy are being actively developed. Automation, autonomy and guided high-throughput techniques are imparting enormous efficiencies and eliminating redundancies in materials synthesis and characterization. The integration of the various parts of the burgeoning ML landscape may lead to materials-savvy digital assistants and to a human–machine partnership that could enable dramatic efficiencies, accelerated discoveries and increased productivity. Here, we review these emergent materials intelligence ecosystems and discuss the imminent challenges and opportunities.

认知计算和人工智能（AI）时代即将来临。受到其成功和承诺的鼓舞，几个AI生态系统正在蓬勃发展，其中许多处于材料科学和工程领域。这些物质情报生态系统由几个独立的发展所塑造。利用机器学习（ML）算法和现有材料数据来创建材料属性和性能预测的替代模型。推动此类替代模型开发的材料数据存储库如雨后春笋般冒出来。正在探索使用自然语言处理方法从文献中自动收集数据和知识（以填充数据存储库）。满足目标特性要求的材料的设计以及创建目标材料的合成步骤似乎可以实现，通过闭环主动学习策略或使用高级生成算法反转预测管道。AI和ML概念也正在改变最初用于创建材料数据的计算和物理实验室基础设施景观。正在积极开发替代模型，这些模型可以使基于物理的模拟（在其上进行训练）的速度提高几个数量级，同时保持精度。自动化，自治和指导性的高通量技术正在赋予巨大的效率，并消除了材料合成和表征中的冗余。新兴的ML环境的各个部分的集成可能会导致精通材料的数字助理以及人机合作关系，从而可以显着提高效率，加快发现速度并提高生产率。在这里，我们回顾了这些新兴的物质智能生态系统，并讨论了迫在眉睫的挑战和机遇。