School of Mathematical & Statistical Sciences Faculty Publications
Document Type
Article
Publication Date
2026
Abstract
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.
Recommended Citation
Hallas, Kristen L., Melissa De Jesus, Christine J. Wu, Jianzhi Li, Jason Bernstein, and Philip C. Myint. "Toward Mapping Multiphase Multicomponent Mixtures with Neural Networks." Data Science in Science 5, no. 1 (2026): 2631836. https://doi.org/10.1080/26941899.2026.2631836
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Publication Title
Data Science in Science
DOI
10.1080/26941899.2026.2631836
Comments
© 2026 The Author(s). Published with license by Taylor & Francis Group, LLC This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.