MixUni: Joint Multi-Property Prediction for Chemical Mixtures with Physics-Informed Heads

Accurate prediction of physico-chemical properties in multi-component liquid mixtures is critical for designing next-generation electrolyte battery materials. Despite recent progress in mixture modeling, existing methods often treat each target property independently, and they struggle to capture the shared molecular mechanisms that couple transport, thermodynamic, and structural properties across heterogeneous mixture systems. To address these limitations, we propose \method, a unified geometry-aware framework for joint multi-property prediction in chemical mixtures. \method combines an E(3)-equivariant molecular encoder with property-conditioned prediction heads, enabling a shared representation across diverse mixture tasks while preserving property-specific inductive biases. In particular, \method incorporates a stretched-Arrhenius conductivity head to model temperature-dependent transport behavior and a sparse Mixture-of-Experts readout to adapt to heterogeneous property regimes. Comprehensive experiments on five CheMixHub mixture-property tasks show that \method consistently improves over single-property geometric baselines, with strong gains in low-data and chemically out-of-distribution settings. These results demonstrate that combining joint representation learning with physically informed readouts provides an effective and general framework for reliable property prediction in complex liquid mixtures.