HEABench:A Multimodal Dataset and Benchmark for High-Entropy Alloy Understanding

High-entropy alloys (HEAs) offer a rich compositional space for materials design, yet their heterogeneous atomic environments make learning structure–property relationships challenging. Progress is constrained in part by limited paired atomic and electronic data and by the lack of consistent protocols for evaluating representations across spatial scales. We introduce HEABench, a multimodal dataset and benchmark of 3,109 quinary, 100-atom configurations spanning 60 elements, with paired structures, total electron charge densities, and property targets, complemented by magnetic labels. HEABench supports global property prediction, local structure–charge matching, and site-resolved magnetic transfer under limited supervision. Experiments show that representing atomic positions as spatial fields via site-centered Gaussians recovers much of the electronic-target fusion benefit, whereas additional charge-density gains depend on the target and evaluation metric. Local matching under configuration and elemental identity controls reveals encoder differences obscured by near-saturated cross-configuration retrieval. With 50 magnetically labeled configurations, structure–charge pretraining reduces site-moment MAE by 10.9% relative to random initialization, using only atomic structures during inference. These findings highlight charge density as a source of transferable supervision beyond its target-dependent benefits as an additional prediction input, and establish HEABench as a testbed for learning structural and electronic representations in chemically disordered alloys. Project page: https://anonymous.4open.science/r/HEABENCH-F6E1.