用维基百科语言模型构建材料图谱，加速金属玻璃发现
Graph Learning Metallic Glass Discovery from Wikipedia
- 用维基百科文本生成元素嵌入，构建材料图网络
- 图神经网络在有限数据下识别隐含材料组合规律
- 多语言嵌入验证自然语言对材料设计的辅助作用
高效合成新材料在多个研究领域需求迫切。但该过程通常耗时且昂贵，尤其对于金属玻璃，其形成高度依赖多种元素的最优组合以抑制结晶。自1960年以来，仅数千种候选材料被探索。近年来，基于机器学习的数据驱动方法为智能材料设计提供了新路径。由于数据稀缺和材料编码不成熟，传统表格数据常由统计学习算法处理，导致模型预测能力和泛化性有限。本文提出从材料网络表示中进行复杂数据学习：节点元素通过语言模型从维基百科编码；设计多种架构的图神经网络作为推荐系统，挖掘材料间的隐藏关系。通过采用不同语言的维基百科嵌入，评估自然语言在材料设计中的潜力。本研究提出一种新范式，用于利用人工智能发掘新型非晶材料及更广泛材料体系。
原文摘要 · Abstract (English)
Synthesizing new materials efficiently is highly demanded in various research fields. However, this process is usually slow and expensive, especially for metallic glasses, whose formation strongly depends on the optimal combinations of multiple elements to resist crystallization. This constraint renders only several thousands of candidates explored in the vast material space since 1960. Recently, data-driven approaches armed by advanced machine learning techniques provided alternative routes for intelligent materials design. Due to data scarcity and immature material encoding, the conventional tabular data is usually mined by statistical learning algorithms, giving limited model predictability and generalizability. Here, we propose sophisticated data learning from material network representations. The node elements are encoded from the Wikipedia by a language model. Graph neural networks with versatile architectures are designed to serve as recommendation systems to explore hidden relationships among materials. By employing Wikipedia embeddings from different languages, we assess the capability of natural languages in materials design. Our study proposes a new paradigm to harvesting new amorphous materials and beyond with artificial intelligence.
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