通过迭代优化科学文本语料库，提升材料性能预测精度。
Iterative Corpus Refinement for Materials Property Prediction Based on Scientific Texts
- 从科学文本中迭代筛选多样化文档，构建更优语料库。
- 在嵌入空间中监测成分-性能关联收敛，实现高精度预测。
- 适用于实验数据稀缺的材料筛选，尤其适合电催化领域。
材料发现与优化受限于元素组合的海量可能性，即所谓的‘组合爆炸’。由于数据稀少，需整合所有可用来源。除了模拟与实验数据外，科学文本中的隐含知识尚未被充分挖掘。本文提出一种迭代语料库精炼框架：通过有策略地选择最多样化的文献，训练Word2Vec模型，并监控嵌入空间中成分-性能相关性的收敛情况。该方法用于预测氧还原（ORR）、析氢（HER）和氧析出（OER）反应中高性能材料，针对大量候选组成进行筛选。结果表明，该方法成功识别出性能最优的材料组合，且经实验室电催化性能实测验证。本研究证实了迭代语料精炼在加速材料发现与优化中的潜力，为缺乏可靠数据的大规模成分空间筛选提供了一种可扩展、高效的技术工具。
原文摘要 · Abstract (English)
The discovery and optimization of materials for specific applications is hampered by the practically infinite number of possible elemental combinations and associated properties, also known as the `combinatorial explosion'. By nature of the problem, data are scarce and all possible data sources should be used. In addition to simulations and experimental results, the latent knowledge in scientific texts is not yet used to its full potential. We present an iterative framework that refines a given scientific corpus by strategic selection of the most diverse documents, training Word2Vec models, and monitoring the convergence of composition-property correlations in embedding space. Our approach is applied to predict high-performing materials for oxygen reduction (ORR), hydrogen evolution (HER), and oxygen evolution (OER) reactions for a large number of possible candidate compositions. Our method successfully predicts the highest performing compositions among a large pool of candidates, validated by experimental measurements of the electrocatalytic performance in the lab. This work demonstrates and validates the potential of iterative corpus refinement to accelerate materials discovery and optimization, offering a scalable and efficient tool for screening large compositional spaces where reliable data are scarce or non-existent.
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