Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks

Equation of state (EOS) tables are commonly used in hydrodynamic simulations of high-pressure, high-temperature phenomena in fields like planetary science, astrophysics, and high-energy-density science. However, generating and storing EOS tables for multiphase, multicomponent mixtures over a wide range of pressures and temperatures is computationally infeasible due to their memory-intensive nature. To address this issue, we have developed a neural network-based machine learning model to predict new EOS tables for binary mixtures. In particular, a deep feedforward neural network trained on a set of ten EOS tables at particular mixture compositions is able to predict nine new (hold-out) EOS tables at different mixture compositions. Specifically, the model predicts the phase diagram, including phase fractions and compositions, of the nine hold-out EOS tables with an accuracy of 98.753%. Overall, this approach is computationally efficient and highly accurate, and one of our goals is to extend it to mixtures with more than two components.