Identification of Liquid-like and Gas-like Particles in Supercritical Fluid via Machine Learning Approach

Abrupt phase transition between vapor and liquid terminates at the liquid-gas critical point, where a single phase of supercritical fluid (SCF) emerges. In this work, adopting machine learning techniques, we propose a novel viewpoint that SCF is an inhomogeneous mixture of liquid-like and gas-like particles. We trained a neural network with local structure data of vapor and liquid particles, generated by molecular dynamics simulations in the near-critical condition; the neural network was trained to label individual particles as 'liquid-like' or 'gas-like'. The trained neural network was then used to identify the coexisting liquid-like and gas-like particles in SCF. The proportion of gas-like particles showed a well-defined dependence on bulk density, agreeing well with the prediction from two-state thermodynamics of interchangeable particles. From the distributions of liquid-like and gas-like particles, we present new explanations on some important properties of SCF, including the local density augmentation and divergent partial molar volume in supercritical solution.