  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
3-cation metal oxide composition spaces. H-CLMP accurately predicts non-linear
composition-property relationships in composition spaces for which no training
data is 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.
