Proton transport and energy deposition in high-energy density matter

Aims and Objectives:

Ion stopping and transport in warm dense matter is a process of fundamental importance for the understanding of the properties of dense plasmas, and for technical application involving inertial confinement fusion research, as well asteroid deflection. Understanding the material properties of metal-rich alloys is of central interest to many applications including planetary science, future explorations of the solar system, studies on the development of Earth's core, super-Earth exoplanets, and advanced material science.

The theoretical description of the ion stopping power in warm dense matter is difficult notably due to strong ion coupling and electron degeneracy. Measurements are still largely missing and this has limited the ability to validate the theoretical models. We expect that such materials will achieve both large densities and temperatures where many body correlations, quantum de-localization and relativistic effects are all important.

Novelty of the research methodology:

In order to address these challenges we will employ a novel simulation approach using a recently developed massively-parallel Molecular Dynamic code. In parallel, we also seek to perform experiments at high-power lasers (such as the EPAC laser at the Rutherford Appleton Laboratory) and accelerators' based facilities (such as CERN, FACET-II or the GSI accelerator complex) to validate the simulation results and the theoretical expectations.

Validation and uncertainty quantification is achieved by constructing a large set of data from either high-quality experiments or numerical simulations. We want to propose a novel machine learning approach to address the complex micro-physics of material strength properties and to identify their emergent behaviour via closed mathematical expressions.

This is done by using a Graph Neural Network (GNN) to represent the discrete description of the underlying continuum system and then applying deep learning techniques to obtain a representation of the material properties as a function of the state variables (temperature, pressure, energy deposition, etc.) The latent representation learned by the GNN is then extracted with a symbolic regression analysis. Our long-term goal is the development of augmented methods to ultimately improve the understanding of ion transport in dense matter.

Because these techniques are very general, we expect the results of this work to have a wide impact well beyond the physical science remit. This project falls within the EPSRC emergence and physics far from equilibrium research area