| Autor: |
J. Hinz, Dayou Yu, Deep Shankar Pandey, Hitesh Sapkota, Qi Yu, D. I. Mihaylov, V. V. Karasiev, S. X. Hu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
APL Machine Learning, Vol 2, Iss 2, Pp 026116-026116-12 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2770-9019 |
| DOI: |
10.1063/5.0192447 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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