Preprint

FZJ-2026-02651

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Differentiable Thermodynamic Phase-Equilibria for Machine Learning

Hicham, K. K. B.RWTH* ; Ascani, M.RWTH* ; Rittig, J. G.RWTH* ; Mitsos, A. (Corresponding author)FZJ*RWTH*

2026
arXiv

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Please use a persistent id in citations: doi:10.48550/ARXIV.2603.11249

Abstract: Accurate prediction of phase equilibria remains a central challenge in chemical engineering. Physics-consistent machine learning methods that incorporate thermodynamic structure into neural networks have recently shown strong performance for activity-coefficient modeling. However, extending such approaches to equilibrium data arising from an extremum principle, such as liquid-liquid equilibria, remains difficult. Here we present DISCOMAX, a differentiable algorithm for phase-equilibrium calculation that guarantees thermodynamic consistency at both training and inference, only subject to a user-specified discretization. The method is rooted in statistical thermodynamics, and works via a discrete enumeration with subsequent masked softmax aggregation of feasible states, and together with a straight-through gradient estimator to enable physics-consistent end-to-end learning of neural $g^{E}$-models. We evaluate the approach on binary liquid-liquid equilibrium data and demonstrate that it outperforms existing surrogate-based methods, while offering a general framework for learning from different kinds of equilibrium data.

Keyword(s): Machine Learning (cs.LG) ; FOS: Computer and information sciences

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Contributing Institute(s):

1. Modellierung von Energiesystemen (ICE-1)

Research Program(s):

1. 899 - ohne Topic (POF4-899) (POF4-899)

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