Year: 2026
Type: preprint
Abstract: Based on SAFT equations of state (EoS), we propose a physics informed neural network that learns the residual Helmholtz free energy, yielding pressure, chemical potentials, entropy, and phase equilibria by automatic differentiation. Transport properties are connected through additional relations. The model can be pretrained on synthetic EoS data and fine-tuned with heterogeneous experimental data. The model operates on logarithmic scale and is designed to stabilize learning properties that span several orders of magnitude between phases, state points, and molecules. A differentiable transport model further allows viscosity and self-diffusion data to learn the temperature derivative of the Helmholtz energy during fine-tuning. As a proof of concept, we fine-tune the model to experimental temperature–pressure–density, viscosity, and self-diffusion data for water. Despite using only state point information and transport data for this single, particularly challenging fluid, the model predicts vapor pressure, vaporization enthalpy, density and entropy anomalies, and the critical temperature without direct exposure to these properties during fine-tuning. This demonstrates that jointly learning Helmholtz energies and transport coefficients is viable and beneficial. We argue that such Helmholtz energy centered, data driven deep learning models provide an alternative to the current proliferation of isolated, single property machine learning solutions. Generalization to unseen molecules and a fixed functional mode with optimized molecule representation parameters are also demonstrated.
Source: ChemRxiv
Authors: Maximilian Fleck, Marcelle B M Spera
Language: English
Subfield: Physical and Theoretical Chemistry
Field: Chemistry
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212154570