  Equations of State (EoS) for fluids have been a staple of engineering design
and practice for over a century. Available EoS are based on the fitting of a
closed-form analytical expression to suitable experimental data. The underlying
mathematical structure and the underlying physical model significantly restrain
the applicability and accuracy of the resulting EoS. This contribution explores
the issues surrounding the substitution of analytical EoS for machine-learned
models, in particular, we describe, as a proof of concept, the effectiveness of
a machine-learned model to replicate statistical associating fluid theory
(SAFT-VR-Mie) EoS for pure fluids. By utilizing Artificial Neural Network and
Gaussian Process Regression, predictions of thermodynamic properties such as
critical pressure and temperature, vapor pressures and densities of pure model
fluids are performed based on molecular descriptors. To quantify the
effectiveness of the Machine Learning techniques, a large data set is
constructed using the comparisons between the Machine-Learned EoS and the
surrogate data set suggest that the proposed approach shows promise as a viable
technique for the correlation, extrapolation and prediction of thermophysical
properties of fluids.
