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Atomic and Laser Physics
Credit: Jack Hobhouse

Daniel Plummer

Graduate Student

Research theme

  • Lasers and high energy density science
  • Plasma physics

Sub department

  • Atomic and Laser Physics

Research groups

  • Laboratory astroparticle physics
  • Oxford Centre for High Energy Density Science (OxCHEDS)
  • Quantum high energy density physics
daniel.plummer@physics.ox.ac.uk
Clarendon Laboratory
  • About
  • Publications

Figure data: A statistical theory of electronic degrees of freedom in wave packet molecular dynamics

University of Oxford (2026)

Abstract:

Figure data relating to "A statistical theory of electronic degrees of freedom in wave packet molecular dynamics".  All data is in the format of .txt files.
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Modeling partially-ionized dense plasma using wavepacket molecular dynamics

(2025)

Authors:

Daniel Plummer, Pontus Svensson, Wiktor Jasniak, Patrick Hollebon, Sam M Vinko, Gianluca Gregori
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Learning heat transport kernels using a nonlocal heat transport theory-informed neural network

Physical Review Research American Physical Society (APS) 7:4 (2025) L042017

Authors:

Mufei Luo, Charles Heaton, Yizhen Wang, Daniel Plummer, Mila Fitzgerald, Francesco Miniati, Sam M Vinko, Gianluca Gregori

Abstract:

<jats:p>We present a data-driven framework for the modeling of nonlocal heat transport in plasmas using a nonlocal theory-informed neural network trained on kinetic particle-in-cell simulations that span both local and nonlocal regimes. The model learns spatio-temporal heat flux kernels directly from simulation data, capturing dynamic transport behaviors beyond the reach of classical formulations. Unlike time-independent kernel models such as Luciani-Mora-Virmont and Schurtz-Nicolaï-Busquet models, our approach yields physically grounded, time-evolving kernels that adapt to varying plasma conditions. The resulting predictions show strong agreement with kinetic benchmarks across regimes. This offers a promising direction for data-driven modeling of nonlocal heat transport and contributes to a deeper understanding of plasma dynamics.</jats:p>
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Time-Embedded Convolutional Neural Networks for Modeling Plasma Heat Transport

(2025)

Authors:

Mufei Luo, Charles Heaton, Yizhen Wang, Daniel Plummer, Mila Fitzgerald, Francesco Miniati, Sam M Vinko, Gianluca Gregori

A molecular dynamics framework coupled with smoothed particle hydrodynamics for quantum plasma simulations

Physical Review Research American Physical Society 7:2 (2025) 023286

Authors:

Thomas Campbell, Pontus Svensson, Brett Larder, Daniel Plummer, Sam Vinko, Gianluca Gregori

Abstract:

We present a novel scheme for modelling quantum plasmas in the warm dense matter (WDM) regime via a hybrid smoothed particle hydrodynamic - molecular dynamic treatment, here referred to as ‘Bohm SPH’. This treatment is founded upon Bohm’s interpretation of quantum mechanics for partially degenerate fluids, does not apply the Born-Oppenheimer approximation, and is computationally tractable, capable of modelling dynamics over ionic timescales at electronic time resolution. Bohm SPH is also capable of modelling non-Gaussian electron wavefunctions. We present an overview of our methodology, validation tests of the single particle case including the hydrogen 1s wavefunction, and comparisons to simulations of a warm dense hydrogen system performed with wave packet molecular dynamics.
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