The development of statistical tools based on machine learning (ML) and deep networks is actively sought for materials design problems. While structure-property relationships can be accurately determined using quantum mechanical methods, these first-principles calculations are computationally demanding, limiting their use in screening a large set of candidate structures. Herein, we use convolutional neural networks to develop a predictive model for the electronic properties of metal halide perovskites (MHPs) that have a billions-range materials design space. We show that a well-designed hierarchical ML approach has a higher fidelity in predicting properties of the MHPs compared to straight-forward methods. In this architecture, each neural network element has a designated role in the estimation process from predicting complex features of the perovskites such as lattice constant and octahedral till angle to narrowing down possible ranges for the values of interest. Using the hierarchical ML scheme, the obtained root-mean-square errors for the lattice constants, octahedral angle and bandgap for the MHPs are 0.01 Å, 5°, and 0.02 eV, respectively. Our study underscores the importance of a careful network design and a hierarchical approach to alleviate issues associated with imbalanced dataset distributions, which is invariably common in materials datasets.

积极寻求基于机器学习（ML）和深度网络的统计工具的开发来解决材料设计问题。虽然可以使用量子力学方法准确确定结构-属性关系，但是这些第一性原理计算在计算上要求很高，从而限制了它们在筛选大量候选结构中的使用。本文中，我们使用卷积神经网络为具有数十亿范围材料设计空间的金属卤化物钙钛矿（MHP）的电子性能开发了预测模型。我们表明，与直接方法相比，精心设计的分层ML方法在预测MHP的属性方面具有更高的保真度。在这种架构中 从预测钙钛矿的复杂特征（例如晶格常数和八面体直到角度）到缩小感兴趣值的可能范围，每个神经网络元素在估计过程中都具有指定的作用。使用分层ML方案，MHP的晶格常数，八面角和带隙的均方根误差分别为0.01Å，5°和0.02 eV。我们的研究强调了谨慎的网络设计和缓解不平衡数据集分布相关问题的分层方法的重要性，这在材料数据集中总是很常见。MHP的八面角和带隙分别为0.01Å，5°和0.02 eV。我们的研究强调了谨慎的网络设计和分层方法来缓解与数据集分布不平衡相关的问题的重要性，这在材料数据集中总是很常见。MHP的八面角和带隙分别为0.01Å，5°和0.02 eV。我们的研究强调了谨慎的网络设计和缓解不平衡数据集分布相关问题的分层方法的重要性，这在材料数据集中总是很常见。