The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning
Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interp...
Saved in:
Published in | APL machine learning Vol. 2; no. 2; pp. 026116 - 026116-12 |
---|---|
Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
United States
AIP Publishing
01.06.2024
AIP Publishing LLC |
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | Ab initio molecular dynamics (AIMD) simulations have become an important tool used in the construction of equations of state (EOS) tables for warm dense matter. Due to computational costs, only a limited number of system state conditions can be simulated, and the remaining EOS surface must be interpolated for use in radiation-hydrodynamic simulations of experiments. In this work, we develop a thermodynamically consistent EOS model that utilizes a physics-informed machine learning approach to implicitly learn the underlying Helmholtz free-energy from AIMD generated energies and pressures. The model, referred to as PIML-EOS, was trained and tested on warm dense polystyrene producing a fit within a 1% relative error for both energy and pressure and is shown to satisfy both the Maxwell and Gibbs–Duhem relations. In addition, we provide a path toward obtaining thermodynamic quantities, such as the total entropy and chemical potential (containing both ionic and electronic contributions), which are not available from current AIMD simulations. |
---|---|
Bibliography: | NA0003856; 2205521 National Science Foundation (NSF) USDOE National Nuclear Security Administration (NNSA) |
ISSN: | 2770-9019 2770-9019 |
DOI: | 10.1063/5.0192447 |