High-precision equation of state benchmark for cryogenic liquid deuterium at ultrahigh pressure

Zhang-Ming, He; Zhang, Qili; Liu, Haifeng; Guo, Jia; Huang, Xiang; Fang, Zhiheng; Xie, Zhiyong; Ye, Junjian; Shu, Hua; Dong, Jiaqin; Zhang, Fan; Tu, Yuchun; Wang, Wei; Fu, Sizu

doi:10.1103/physrevb.103.134107

Cited by 4 publications

(1 citation statement)

References 49 publications

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“…These methods are driven by density functional theory (DFT), where advanced meta-generalized gradient approximation (meta-GGA) exchange-correlation (XC) free energy density functional TSCANL [10], a high-accuracy non-interacting orbital-free free energy density functional LKTFγTF (see details in [7]) is used. Compared to previous models [11][12][13][14][15], iFPEOS showed better agreement with experimental measurements [4,[16][17][18] for temperatures T ∼ 60 000 K and pressures up to P ∼ 200 GPa. However, for higher T − P regimes, iFPEOS fails to close the gap between latest theory and experiment.…”

Section: Introductionmentioning

confidence: 45%

Deep energy-pressure regression for a thermodynamically consistent EOS model

Yu,

Shankar Pandey,

Hinz

et al. 2024

Mach. Learn.: Sci. Technol.

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In this paper, we aim to explore novel machine learning (ML) techniques to facilitate and accelerate the construction of universal Equation-Of-State (EOS) models with a high accuracy while ensuring important thermodynamic consistency. When applying ML to fit a universal EOS model, there are two key requirements: (1) a high prediction accuracy to ensure precise estimation of relevant physics properties and (2) physical interpretability to support important physics-related downstream applications. We first identify a set of fundamental challenges from the accuracy perspective, including an extremely wide range of input/output space and highly sparse training data. We demonstrate that while a neural network (NN) model may fit the EOS data well, the black-box nature makes it difficult to provide physically interpretable results, leading to weak accountability of prediction results outside the training range and lack of guarantee to meet important thermodynamic consistency constraints. To this end, we propose a principled deep regression model that can be trained following a meta-learning style to predict the desired quantities with a high accuracy using scarce training data. We further introduce a uniquely designed kernel-based regularizer for accurate uncertainty quantification. An ensemble technique is leveraged to battle model overfitting with improved prediction stability. Auto-differentiation is conducted to verify that necessary thermodynamic consistency conditions are maintained. Our evaluation results show an excellent fit of the EOS table and the predicted values are ready to use for important physics-related tasks.

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Section: Introductionmentioning

confidence: 45%

Deep energy-pressure regression for a thermodynamically consistent EOS model

Yu,

Shankar Pandey,

Hinz

et al. 2024

Mach. Learn.: Sci. Technol.

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Imaging velocity interferometer system for any reflector (VISAR) diagnostics for high energy density sciences

Celliers

Millot

2023

Review of Scientific Instruments

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Two variants of optical imaging velocimetry, specifically the one-dimensional streaked line-imaging and the two-dimensional time-resolved area-imaging versions of the Velocity Interferometer System for Any Reflector (VISAR), have become important diagnostics in high energy density sciences, including inertial confinement fusion and dynamic compression of condensed matter. Here, we give a brief review of the historical development of these techniques, then describe the current implementations at major high energy density (HED) facilities worldwide, including the OMEGA Laser Facility and the National Ignition Facility. We illustrate the versatility and power of these techniques by reviewing diverse applications of imaging VISARs for gas-gun and laser-driven dynamic compression experiments for materials science, shock physics, condensed matter physics, chemical physics, plasma physics, planetary science and astronomy, as well as a broad range of HED experiments and laser-driven inertial confinement fusion research.

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Improved first-principles equation-of-state table of deuterium for high-energy-density applications

Mihaylov

Karasiev

et al. 2021

Phys. Rev. B

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High-precision equation of state benchmark for cryogenic liquid deuterium at ultrahigh pressure

Cited by 4 publications

References 49 publications

Deep energy-pressure regression for a thermodynamically consistent EOS model

Deep energy-pressure regression for a thermodynamically consistent EOS model

Imaging velocity interferometer system for any reflector (VISAR) diagnostics for high energy density sciences

Improved first-principles equation-of-state table of deuterium for high-energy-density applications

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