The adoption of machine learning in materials science has rapidly transformed materials property prediction. Hurdles limiting full capitalization of recent advancements in machine learning include the limited development of methods to learn the underlying interactions of multiple elements as well as the relationships among multiple properties to facilitate property prediction in new composition spaces. To address these issues, we introduce the Hierarchical Correlation Learning for Multi-property Prediction (H-CLMP) framework that seamlessly integrates: (i) prediction using only a material's composition, (ii) learning and exploitation of correlations among target properties in multi-target regression, and (iii) leveraging training data from tangential domains via generative transfer learning. The model is demonstrated for prediction of spectral optical absorption of complex metal oxides spanning 69 three-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear composition-property relationships in composition spaces for which no training data are available, which broadens the purview of machine learning to the discovery of materials with exceptional properties. This achievement results from the principled integration of latent embedding learning, property correlation learning, generative transfer learning, and attention models. The best performance is obtained using H-CLMP with transfer learning [H-CLMP(T)] wherein a generative adversarial network is trained on computational density of states data and deployed in the target domain to augment prediction of optical absorption from composition. H-CLMP(T) aggregates multiple knowledge sources with a framework that is well suited for multi-target regression across the physical sciences.

中文翻译：

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用于多属性预测的材料表示和迁移学习

机器学习在材料科学中的应用迅速改变了材料特性预测。限制机器学习最新进展完全资本化的障碍包括学习多个元素的潜在相互作用以及多个属性之间的关系以促进新组合空间中的属性预测的方法的有限发展。为了解决这些问题，我们引入了多属性预测的分层相关学习 (H-CLMP) 框架，该框架无缝集成：(i) 仅使用材料成分进行预测，(ii) 学习和开发多属性中目标属性之间的相关性。目标回归，以及 (iii) 通过生成迁移学习利用来自切向域的训练数据。该模型用于预测跨越 69 个三阳离子金属氧化物组成空间的复杂金属氧化物的光谱光吸收。H-CLMP 准确预测了没有可用训练数据的成分空间中的非线性成分-特性关系，这将机器学习的范围扩大到发现具有特殊特性的材料。这一成就源于潜在嵌入学习、属性相关学习、生成迁移学习和注意力模型的原则性整合。使用 H-CLMP 和转移学习 [H-CLMP(T)] 获得最佳性能，其中生成对抗网络在状态数据的计算密度上进行训练并部署在目标域中，以增强对组合光吸收的预测。