Helmholtz Association funds project for data acquisition using neural networks In addition to experimentally generated data,…
Matter under Extreme Conditions
Scientists at CASUS investigate the non-equilibrium behavior of matter under extreme conditions by developing innovative electronic structure methods for the numerical modeling of high energy density (HED) phenomena in warm dense matter (WDM) induced by extreme electromagnetic fields, temperatures and pressures.
They contribute to answering fundamental science questions and enabling novel technologies. Their research activities revolve around topics such as:
- Weakly and strongly correlated quantum systems under extreme conditions
- Dynamics of quantum systems at and beyond local thermal equilibrium
- Astrophysical processes in stellar environments
- Structure of planetary cores and formation processes of planets
- Novel materials discovery at multiple length scales
- Plasma-based compact particle accelerators for applications such as tumor radiation therapy or compact X-ray light sources
Our projects contribute to accomplishing an accurate and consistent description of HED phenomena across multiple length and time scales:
• Ab-initio Path Integral Monte Carlo Simulations of WDM
We develop and perform exact large-scale simulations of correlated electrons and multi-component systems in the WDM regime. In doing so, we provide accurate benchmark data and pivotal input for other theory frameworks such as DFT and TDDFT.
• Inverse Problem Solving and Reconstruction
We use state-of-the-art numerical methods to solve inverse problems such as the reconstruction of the dynamical structure factor from quantum Monte-Carlo data for imaginary-time correlation functions.
• Density Functional Methodologies under HED Conditions
We develop and apply density functional methodologies (DFT-MD and TDDFT) in order to compute equation-of-state data and electronic transport properties (stopping powers, the electrical conductivity, and the dynamical structure factor) in the WDM regime.
• Machine-Learning DFT Simulation Package for HED Phenomena
We develop a physics-informed machine-learning framework to accelerate traditional DFT-MD simulations. Based on state-of-the-art neural network models, we enable the calculation of energies and forces in atomic configurations at a scale and cost unattainable with direct DFT algorithms.
• Multiscale HED Matter Simulation Framework
We develop a multiscale materials modeling simulation framework for WDM in order to bridge the gap between atomistic simulations on the microscopic scale and device-level simulations on the macroscopic scale. We generate interatomic potentials by combining numerical methods for high-fidelity data generation with machine-learning models and apply them within large-scale molecular dynamics simulations.
• Surrogate Models for WDM
We develop accurate and computationally efficient surrogate models for WDM. They serve as a testing ground for modeling nonequilibrium behavior beyond local thermal equilibrium and complement state-of-the-art magnetohydrodynamics codes with on-the-fly data.
• Theoretical Support for XRTS Experiments
With our combined numerical modeling capabilities, we support the successful characterization of HED phenomena generated in laboratories such as coherent light sources and pulsed power facilities. We develop plasma diagnostics codes to support state-of-the-art experiments in the WDM regime with cutting-edge theory.
CASUS researchers develop effective tool to describe exotic state of matter The study of warm dense…
We are happy to host the Workshop on “Data-driven simulation and PDE learning using Physics-informed neural…