Hugoniot Equation of State Model for Mixtures

High-throughput computing has become a cornerstone of modern materials design and is driving new advances in the study of shock-compressed matter. Central to these efforts is an accurate Hugoniot equation of state (EOS) for mixtures, yet existing mixture models continue to show sizeable scatter. Here we benchmarked two widely used schemes—the volume-additive model (Mod A) and the isothermal-average model (ModⅠ)—against experimental Hugoniot data for binary alloys, ternary alloys and granular mixtures. The Mod A model assumes full thermodynamic equilibrium and neglects the temperature rise of individual constituents under shock compression. The ModⅠ model, by contrast, removes this thermal contribution by deriving the mixture Hugoniot from 0 K isotherms via the Mie-Grüneisen EOS. Systematic comparison between the predicted Hugoniot EOS of binary alloy, ternary alloy, granular mixtures and the experimental data reveals that the ModⅠ model reproduces measured Hugoniot states within about 10% error across the entire pressure range studied, outperforming the Mod A model in both accuracy and robustness. Both approaches exhibit moderately larger discrepancies at low shock pressures, where thermal effects are most pronounced.