The development of thermodynamically consistent and physics-informed equation-of-state model through machine learning

Journal Article · 06 May 2024 · APL Machine Learning

DOI: https://doi.org/10.1063/5.0192447 · OSTI ID:2564496

Hinz, J.; Yu, Dayou ; Pandey, Deep Shankar ; Sapkota, Hitesh ; Yu, Qi; Mihaylov, D. I. ; Karasiev, V. V. ; Hu, S. X.

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.

• Research Organization: University of Rochester, NY (United States)
• Sponsoring Organization: National Science Foundation (NSF); USDOE; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: NA0003856
• OSTI ID: 2564496
• Journal Information: APL Machine Learning, Journal Name: APL Machine Learning Journal Issue: 2 Vol. 2; ISSN 2770-9019
• Publisher: American Institute of PhysicsCopyright Statement
• Country of Publication: United States
• Language: English