用可解释机器学习找出钙钛矿和萤石材料中氧扩散活化能的关键因素。
Explainable Machine Learning for Oxygen Diffusion in Perovskites and Pyrochlores
- 通过分组算法提取材料属性特征，训练七种模型找关键影响因素。
- 钙钛矿中决定活化能的是A位离子键离子性与氧分压，萤石则看A位价电子数和B位电负性。
- 重要特征均来自元素金属性质的加权平均，反直觉但可快速筛选高性能材料。
可解释机器学习有助于发现材料性能的新物理关系。为理解钙钛矿和萤石中氧扩散活化能的决定因素，我们构建了实验活化能数据库，并对材料属性特征应用分组算法。这些特征用于拟合七种不同的机器学习模型。集成共识表明，预测活化能最重要的特征是钙钛矿的A位键离子性和氧分压；对于萤石，最重要的是A位s价电子数和B位电负性。所有关键特征均由元素金属性质的加权平均构造而成，尽管构成氧化物的二元氧化物性质也包含在特征集中。这令人意外，因为二元氧化物性质比金属性质更接近实验测得的材料性能。本研究识别出易于测量的特征，可实现快速筛选具有高氧离子扩散速率的新材料。
原文摘要 · Abstract (English)
Explainable machine learning can help to discover new physical relationships for material properties. To understand the material properties that govern the activation energy for oxygen diffusion in perovskites and pyrochlores, we build a database of experimental activation energies and apply a grouping algorithm to the material property features. These features are then used to fit seven different machine learning models. An ensemble consensus determines that the most important features for predicting the activation energy are the ionicity of the A-site bond and the partial pressure of oxygen for perovskites. For pyrochlores, the two most important features are the A-site $s$ valence electron count and the B-site electronegativity. The most important features are all constructed using the weighted averages of elemental metal properties, despite weighted averages of the constituent binary oxides being included in our feature set. This is surprising because the material properties of the constituent oxides are more similar to the experimentally measured properties of perovskites and pyrochlores than the features of the metals that are chosen. The easy-to-measure features identified in this work enable rapid screening for new materials with fast oxide-ion diffusivity.
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。