  High-entropy materials (HEMs) have recently emerged as a significant category
of materials, offering highly tunable properties. However, the scarcity of HEM
data in existing density functional theory (DFT) databases, primarily due to
computational expense, hinders the development of effective modeling strategies
for computational materials discovery. In this study, we introduce an open DFT
dataset of alloys and employ machine learning (ML) methods to investigate the
material representations needed for HEM modeling. Utilizing high-throughput DFT
calculations, we generate a comprehensive dataset of 84k structures,
encompassing both ordered and disordered alloys across a spectrum of up to
seven components and the entire compositional range. We apply descriptor-based
models and graph neural networks to assess how material information is captured
across diverse chemical-structural representations. We first evaluate the
in-distribution performance of ML models to confirm their predictive accuracy.
Subsequently, we demonstrate the capability of ML models to generalize between
ordered and disordered structures, between low-order and high-order alloys, and
between equimolar and non-equimolar compositions. Our findings suggest that ML
models can generalize from cost-effective calculations of simpler systems to
more complex scenarios. Additionally, we discuss the influence of dataset size
and reveal that the information loss associated with the use of unrelaxed
structures could significantly degrade the generalization performance. Overall,
this research sheds light on several critical aspects of HEM modeling and
offers insights for data-driven atomistic modeling of HEMs.
