The interpretability of machine learning reveals associations between input features and predicted physical properties in models, which are essential for discovering new materials. However, previous works were mainly devoted to algorithm improvement, while the essential multi-scale characteristics are not well addressed. This paper introduces distortion modes of oxygen octahedrons as cross-scale structural features to bridge chemical compositions and material properties. Combining model-agnostic interpretation methods, we are able to achieve interpretability even using simple machine learning schemes and develop a predictive model of effective mass for a widely used material type, namely perovskite oxides. With this framework, we reach the interpretability of the model, understanding the trend of the effective mass without any prior background information. Moreover, we obtained the knowledge only available to experts, i.e., the interpretation of effective mass from the s–p orbitals hybridization of B-site cations and O2− in ABO3 perovskite oxides.

中文翻译：

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具有跨尺度特征的钙钛矿氧化物有效质量的可解释机器学习模型


机器学习的可解释性揭示了输入特征与模型中预测的物理特性之间的关联，这对于发现新材料至关重要。然而，以前的工作主要致力于算法改进，而基本的多尺度特性并没有得到很好的解决。本文介绍了氧八面体的畸变模式作为跨尺度结构特征，以桥接化学成分和材料特性。结合与模型无关的解释方法，我们甚至能够使用简单的机器学习方案实现可解释性，并为广泛使用的材料类型（即钙钛矿氧化物）开发有效质量的预测模型。有了这个框架，我们实现了模型的可解释性，在没有任何先前背景信息的情况下理解了有效质量的趋势。此外，我们获得了只有专家才能获得的知识，即从 ABO3 钙钛矿氧化物中 B 位阳离子和 O2− 的 s-p 轨道杂交中解释有效质量。