We show that unsupervised machine learning (ML) using principal-component (PC) analysis provides a straightforward pathway for developing accurate and interpretable electronic-structure descriptors of the chemical and catalytic properties of materials. We demonstrate the approach by finding chemisorption descriptors for metal alloys and surface oxygens on metals and metal oxides. In both cases, the PC descriptors yield ML models that predict the material's chemical properties with competitive accuracy compared with ML models built using established descriptors. Importantly, interpreting the electronic-structure patterns captured by each PC descriptor via signal reconstruction suggests potential design motifs for future electronic-structure descriptor design and allows us to identify links between a material's geometric and catalytic properties. Ultimately, we show that the unsupervised ML approach provides a route to find electronic-structure descriptors of the catalytic properties of materials that readily connect to geometric structure and composition.

我们表明，使用主成分 (PC) 分析的无监督机器学习 (ML) 为开发材料化学和催化性能的准确和可解释的电子结构描述符提供了一种直接的途径。我们通过找到金属合金和金属和金属氧化物上的表面氧的化学吸附描述符来证明该方法。在这两种情况下，与使用已建立的描述符构建的 ML 模型相比，PC 描述符生成的 ML 模型能够以具有竞争力的准确性预测材料的化学性质。重要的是，通过信号重建解释每个 PC 描述符捕获的电子结构模式为未来的电子结构描述符设计提供了潜在的设计主题，并使我们能够识别材料之间的联系。s 几何和催化性能。最终，我们表明无监督 ML 方法提供了一种途径，可以找到易于连接到几何结构和组成的材料催化特性的电子结构描述符。