EquiNet: Modeling Vapor-Liquid Equilibrium Using Simultaneous Neural-Network Predictions for Activity Coefficient and Vapor Pressure

In this work, we present EquiNet, a neural network for predicting vapor–liquid equilibrium (VLE) in novel binary mixtures through direct estimation of activity coefficients and vapor pressures. The model embeds a classic excess-Gibbs free energy formulation, ensuring Gibbs–Duhem consistency on all predicted activity coefficients. Given molecular SMILES, liquid-phase composition, and temperature, EquiNet predicts vapor composition and pressure, enabling end-to-end training on experimental data without requiring precomputed activity coefficients. Across the UNIFAC-scope VLE dataset (covering 55,470 binary datapoints), EquiNet achieves a log10 Pressure (Pa) RMSE of 0.0659, outperforming UNIFAC (0.087) and the HANNA machine-learning model (0.101) under the same molecular-scope assumptions. For evaluation on previously unseen chemical systems, we follow a novel-mixture cross-validation protocol that demonstrated strong transferability across diverse functional groups and molecular classes. On the public Jaubert benchmark, restricted to UNIFAC-compatible pairs (2,099 points), EquiNet attains a comparable 0.03436 log10 Pressure (Pa) RMSE, substantially below HANNA (0.26743) and UNIFAC (1.55616). This work employs a modified Raoult’s law approach without vapor-phase fugacity or Poynting corrections and is limited to neutral, organic, non-reactive binary mixtures composed of supported elements. The method does not detect mixture criticality, and any supercritical examples should be interpreted qualitatively.