用机器学习发现更可持续的能源材料，模型还能理解材料间的化学物理关系。
Discovery of sustainable energy materials via the machine-learned material space
- 用图注意力网络学习材料空间，捕捉化学与物理规律。
- 对近万种材料按光学性质和化学相似性聚类，发现可持续替代品。
- 方法通用，适用于任何先进模型，适合材料设计与能源研究者。
机器学习模型能否真正理解材料空间？我们以OptiMate模型为例，该模型基于图注意力网络，用于预测半导体与绝缘体的光学性质。通过将模型的潜在嵌入进行UMAP降维，我们证明其能捕捉到细腻且可解释的材料空间表征，反映化学与物理原理，且无用户偏见。这使得近10,000种材料可根据光学性质和化学相似性进行聚类。此外，我们展示了如何利用学习到的材料空间，为光伏等能源技术中的关键材料寻找更可持续的替代品。结果表明，机器学习在材料科学中具有双重价值：精准预测材料性质的同时，揭示材料空间的内在结构。该方法具备广泛适用性，可推广至任何先进机器学习模型，助力多样应用中的材料发现与设计。
原文摘要 · Abstract (English)
Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attention network trained to predict the optical properties of semiconductors and insulators. By applying the UMAP dimensionality reduction technique to its latent embeddings, we demonstrate that the model captures a nuanced and interpretable representation of the materials space, reflecting chemical and physical principles, without any user-induced bias. This enables clustering of almost 10,000 materials based on optical properties and chemical similarities. Beyond this understanding, we demonstrate how the learned material space can be used to identify more sustainable alternatives to critical materials in energy-related technologies, such as photovoltaics. These findings demonstrate the dual utility of machine learning models in materials science: Accurately predicting material properties while providing insights into the underlying materials space. The approach demonstrates the broader potential of leveraging learned materials spaces for the discovery and design of materials for diverse applications, and is easily applicable to any state-of-the-art machine learning model.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。