Data-driven approaches to materials exploration and discovery are building momentum due to emerging advances in machine learning. However, parsimonious representations of crystals for navigating the vast materials search space remain limited. To address this limitation, we introduce a materials discovery framework that utilizes natural language embeddings from language models as representations of compositional and structural features. The contextual knowledge encoded in these language representations conveys information about material properties and structures, enabling both similarity analysis to recall relevant candidates based on a query material and multi-task learning to share information across related properties. Applying this framework to thermoelectrics, we demonstrate diversified recommendations of prototype crystal structures and identify under-studied material spaces. Validation through first-principles calculations and experiments confirms the potential of the recommended materials as high-performance thermoelectrics. Language-based frameworks offer versatile and adaptable embedding structures for effective materials exploration and discovery, applicable across diverse material systems.

由于机器学习的新兴进步，数据驱动的材料探索和发现方法正在积聚动力。然而，用于在广阔的材料搜索空间中导航的晶体的简约表示仍然有限。为了解决这一限制，我们引入了一种材料发现框架，该框架利用语言模型中的自然语言嵌入作为组成和结构特征的表示。这些语言表示中编码的上下文知识传达有关材料属性和结构的信息，使相似性分析能够基于查询材料回忆相关候选者，并支持多任务学习来共享相关属性的信息。将该框架应用于热电学，我们展示了原型晶体结构的多样化建议，并确定了未充分研究的材料空间。通过第一原理计算和实验的验证证实了推荐材料作为高性能热电材料的潜力。基于语言的框架为有效的材料探索和发现提供了多功能且适应性强的嵌入结构，适用于不同的材料系统。